Relatron: Automating Relational Machine Learning over Relational Databases
Authors: Zhikai Chen
Han Xie
Jian Zhang
Jiliang Tang
Xiang song
Huzefa Rangwala
Venue: ICLR 2026 Poster (2026)
Keywords: AutoML
Relational database
Relational deep learning
Graph machine learning
Tabular machine learning
Primary area: learning on graphs and other geometries & topologies

Abstract: Predictive modeling over relational databases (RDBs) powers applications in various domains,
yet remains challenging due to the need to capture both cross-table dependencies and complex feature interactions. Recent Relational Deep Learning (RDL) methods automate feature engineering via message passing, while classical approaches like Deep Feature Synthesis (DFS) rely on predefined non-parametric aggregators.
Despite promising performance gains, the comparative advantages of RDL over DFS and the design principles for selecting effective architectures remain poorly understood.
We present a comprehensive study that unifies RDL and DFS in a shared design space and conducts large-scale architecture-centric searches across diverse RDB tasks. Our analysis yields three key findings: (1) RDL does not consistently outperform DFS, with performance being highly task-dependent; (2) no single architecture dominates across tasks, underscoring the need for task-aware model selection; and (3) validation accuracy is an unreliable guide for architecture choice.
This search yields a curated model performance bank that links model architecture configurations to their performance; leveraging this bank, we analyze the drivers of the RDL–DFS performance gap and introduce two task signals—RDB task homophily and an affinity embedding that captures size, path, feature, and temporal structure—whose correlation with the gap enables principled routing. Guided by these signals, we propose Relatron, a task embedding-based meta-selector that first chooses between RDL and DFS and then prunes the within-family search to deliver strong performance. Lightweight loss-landscape metrics further guard against brittle checkpoints by preferring flatter optima. In experiments, Relatron resolves the “more tuning, worse performance” effect and, in joint hyperparameter–architecture optimization, achieves up to 18.5\% improvement over strong baselines with $10\times$ lower computational cost than Fisher information–based alternatives. Our code is available at https://github.com/amazon-science/Automating-Relational-Machine-Learning.

Published as a conference paper at ICLR 2026
RELATRON: AUTOMATING RELATIONAL MACHINE LEARNING OVER RELATIONAL DATABASES
Zhikai Chen Han Huzefa Rangwala
Han Xie², Jian Zhang?, Jilling Tang', Xiang Song",
Michigan State University AAmazon
ABSTRACT
Predictive modeling over relational databases (RDBs) powers applications in var- ious domains, yet remains challenging due to the need to capture both cross- table dependencies and complex feature interactions. Recent Relational Deep Learning (RDL) methods automate feature engineering via message passing, while classical approaches like Deep Feature Synthesis (DFS) rely on prede- fined non-paraments parametric aggregators. Despite promising performance gains, the comparative advantages of RDL over DFS and the design principles for select- ing effective architectures remain poorly understood. We present comprehen- sive study that unifies RDL and DFS in a shared design space and conducts large-scale architecture-centri searches across diverse RDB tasks. Our analy- sis yields three key findings: (1) RDL does not consistently outperform DFS, with performance being highly task-dependent; (2) no single architecture domi- nates across tasks, underscoring the need for task-aware model selection; and (3) validation accuracy is an unreliable guide for architecture choice. This search yields curated model performance bank that links model architecture config- urations to their performance; leveraging this bank, we analyze the drivers of the RDL-DFS performance gap and introduce mophily and an affinity embedding that captures size, path, feature, and tempo- ral stuuturr-whit
task signals-RID RDB task ho-
correlation with the gap enables principled routing. Guided by these signals, we propose Relatron, a task embedding- based meta-selector that first chooses between RDL and DFS and then prunes the within-family search to deliver strong performance. Lightweight loss-landscape metrics fur- ther guard against brittle checkpoints by preferring flatter optima. In experi- ments, Relatron resolves the "more tuning, worse performance" effect and, in joint hyperparameter-archiine architecture optimization, achieves up to 18.5% improve- ment over strong baselines with 10x lower computational cost than Fisher infor- based alternatives. Our code is available at htt tttbbbtttttuuggggo//g com/ amazon sc ence/ Automat ng Relat ona Mach ne Learning
INTRODUCTION
Relational databases (Codd) 2007 Harrington, 2016) have served as the foundation of data manage- ment for decades, organizing interconnected information through tables, primary keys, and for- cign keys (Harrington) 2016). Their support for data integrity, consistency, and complex SQL Johnson al. 2016), academic research (Melvin 2025). and business applications (Stroe 2011 However, as data volume and complexity grow, traditional analytics fall short, creating demand for machine learning to identify patterns, automate decisions, and generate scalable insights. The conventional approach requires practitioners to manually export and flatten relational data into single tables through custom joins and feature engineering (L.mm et al. 2017) before applying tabular MI. methods.
queries has made them essential across healthcare (Whitel 2020
At the macro level, two lines of work aim reduce manual flattening and feature engineering in RDBs: (i) deep feature synthesis (DFS) (Kanter & Veeramachaneni 2015) and (ii) relational
Work done while interning at Amazon. Email: chenzh8 Grrenzh85eiin Semsu.mmmuueeee. Corresponding author.

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Published as a conference paper at ICLR 2026
deep learning (RDL) (Robinson et al. 2024 Fey et al. 2024). Both operate heterogeneous tity-relation graphs induced from the underlying database schema, where rows are represented as nodes typed by their tables and foreign-key links represented typed edges. DFS program- matically composes relational primitives (e.g., aggregations along join paths) to produce a single feature table on which standard tabular learner is trained. RDL trains graph neural networks (GNNs) (Kipf & Welling 2017 Hamilton et al., 2017) end-to-end on this heterogeneous graph, learning lask-specific aggregations via message passing. Empirically, both families exploit the re- lational structure and can surpass relation-agnostic baselines on several RDB benchmarks (Wang) ct al.
2024a Robinson et al. 2024p.
However, no comprehensive comparison exists between these paradigms to clarify when each per- forms better or their relative advantages for different task types. Practitioners currently lack prin- cipled guidelines for choosing between DFS and RDL when tackling relational database prediction tasks. Additionally, methods for selecting specific design components- such as message-passingg functions in RDL or tabular models in ture selection for RDB tasks labor-intensive process that relies heavily on expert knowledge.
largely unexplored. These gaps make architec-
Design space and evaluation. To bridge this gap, we first propose representative design space for RDL and DFS: for the former, we decompose models into (1) feature ancoding/augmentatiin,, (2) message passing, and (3) tusk-specific readouts; for DFS, we use non- aa--arametric feature engineer- ing paired with a tabular model. We conduct an architecture-centric search-a -a grid over architecture choices with sampled hyperparameters- build performance bank. Key findings: (1) Brute-force search outperforms from-scratch RDL baselines, validating the proposed design space. (2) At the macro level (DFS vs RDL), RDL wins on more tasks (though both have distinct strengths); at the micro level (fine-grained model architectures), neither family has a single best design. (3) Validation performance can be unreliable for selection, leading degraded test performance.
Automatic architecture selection. We identify factors that drive the performance gap and use them to design the Relatron, an architecture selector with strong test generalization. We introduce an RDB-task homophily metric that correlates strongly with the performance gap between DFS and RDL, further enriched with training-frec affinity embeddings that capture task table size, structural affinity, and temporal dynamics. We further observe that the generalization behavior of configura- tions (validation-select) vs. test-selected) is reflected in the loss landscape geometry. Accordingly, propose lundsaape-dervve metric for more reliable post-selection. Combined, our pipeline performs strongly on real-workh world RDB tasks, matching or exceeding prior methods (Cao et al. [003] Achille et al., (019)) in task-embedding quality and in predicting whether RDL or DFS preferable; for joint hyperparameter and architecture search, it outperforms strong baselines, including search- based and eeweedingg-batt ones (Cao et al.) 2023 Bischl et al. 2023), while using up 10x less compute resources than task-embedding based methods.
Our contributions can be summarized as follows.
1. We propose representative model design space for RDB predictive tasks, featuring promising performance, and generate a model performance bank that links model architecture configura- tions to task performance for future research.
2. Based on a comprehensive search on the model design space, we point out the limitations of RDL, and propose a routing method to select between RDL and DFS for RDB predictive tasks automatically. Furthermore, we analyze the factors that drive the performance gap between RDL and DFS, and these insights can inspire further research, such as the development of relational foundation models.
3. Through extensive experiments, we validate the effectiveness of our pipelines in tasks such as predicting proper architectures and joint hwwppparraamteer-rrctttttttt search.
RELATED WORK AND BACKGROUND
In this section, we present related works necessary for understanding the following paper contents, and put other related works in Appendix D
'The graph machine learning models studied in Wang et al. 2025b) differ from RDL Robinson et al. 2024 in implementation details, discussed further in Appendix
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DFS
Popslation
State
ID
City
X0XX
Primary Key
Foreign Key
Oio
AZ
Denott
10000
A3
Student
Course
Depth
Depth
Nome
Mojor
ID
Course
Prot.
City omae
CityFasulation
Stete
COUNTIGNY
MEANIGiy XX0
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Hoth
AJ
Scm
Aich
1300
Momo
CS
08
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DNe
5000
Stove
ME
LLN
Kon
RDL
Grade
Ditt
ID
c,10
Grad
Date
$,1D
lubel
1/3
A+
feature encoder
Tebular model
feature
Readout
level
1
A+
1/1
2/1
(a)
(h)
Figure I: (a) An example of generating the task table from an RDB. The label is based on whether student has achieved A+ in course before specific timestamp. (b) Another example demon- strating the working process of DFS and RDL. For DFS, predefined set of aggregation functions, such MEAN and COUNT, is used aggregate information across multiple tables based key relationships into a final data table. For comparison, RDL is claimed to replace the manual aggrega- tion design with automatic cassage-passing-baae sparse attention.
Relational Deep Learning (RDL) RRobinson al. 2024 Fey et al. 2024) applies graph machine learning to relational databases. RDB prediction has three key traits (Figure [ (1) time is first- class-laaeels are split and conditioned on time; (2) labels are defined by ume-constrained SQL over arbitrary column combinations; (3) heterogeneous column types make feature interactions richer than in text-attributed or non non-attributed graphs. These choices mirror real industrial settings. Waan ct al. [2024a] 2025b) offer view closer to traditional heterogencous GNNs; although Wang et al. (2025b) reports results on Relbench (Robinson et al. 2024), the modeling and evaluation setups differ. We therefore re-implement all methods in the unified framework for fair comparison. Some recent RDL works center on specialized models for RDL, including higher-order message pass- ing (Chen et al. |l.||222.5533 (025a) and recommendation (Yuan al. 2024). Transformers and LLMs (Dwivedi iet al. 2005 Wu et al.) 2025) Wydmuch et al. (2024) have been tested, but are resource-heavy with modest gains. Beyond RDB- RDB-specific studies, broader graph foundation model and graph-LLM LLM lit- erature provides useful context on transferable graph pre- pre-training and prompting paradigms (Mao al. 2024, Chen et al. 2023 Wang al., 2025a). Foundation models include Griffin (Wang al., 2025b), which uses table attention yet often fails to beat GNNs, and KumoRFM (Fey et al., 2025]. graph transformer with strong performance and in-context learning, though details remain undisclosed. In this paper, we focus efficient models training from scratch, leaving foundation models for future work.
Deep Feature Synthesis (DFS). Compared to RDL, DFS (Kanter & Veeramachaneni 2015) is an ofeen-overlooked approach that aggregates cross-tabl table information into single target table via auto- mated feature engineering (Zhao al. 2020 Lam et al.; 2017 2018). It underpins commercial sys- tems such as getm F Given a target table and schema graph, DFS traverses foreign-key-priman key links and composes type-aware primitives into feature definitions. Transform primitives oper- ate on single columns, while aggregation primitives (e.g., statistics such as MAX, MIN, MODE) summarize sets of related rows; compositions along schema paths yield higher-order features. For time-indexed tasks, DFS evaluates every recipe under per-row cutoff time, ensuring that only in- formation available the past contributes the feature value, thereby avoiding leakage.
3 DESIGN SPACE OF MODEL ARCHITECTURES OVER RDB
Architecture selection begins by giving an architecture design space. This section introduces the task and design space, then presents evaluation results and observations from the exploration.
PDD denotes both the learning paradigm and the problem setting; we use RDL for the former RDB for the latter.
http://ggemll.co/aaateeeee
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3.1 PREDICTIVE TASKS ON RDBs
Problem definition. A relational database (RDB) is a tuple D - (T,L), where T (Ti, ..I....,TT,. Tu. is set of tables and & C T x T is set of links between them. Each table T; T consists of rows (entities) {ri...., "m. J. Links are related to primary keys (PKs) and foreign keys (FKs). A PK pv uniquely identifies a row, while a FK establishes link to a row in another table by referencing its PK. Each row also has a set of non-key attributes, Te, and an optional timestamp, tz. A temporal predictive task II with respect to time lpred can be defined over two granularities. Entity- level prediction learns function f Digrea x Viarget - y that maps entities from target set Vtarget Ç Ti to a label space y. Link-level prediction determines the existence of a link between two entities, v; E Ti and v/ E T, at time pred by learning function f" DDirred X X;; x xT, - {0, 1). RDB tables can be categorized into fact tables and dimension tables. A fact table stores events or transactions (e.g., purchases, clicks, race results) with many rows, and each typically carries foreign keys to several entities. A dimension table stores descriptive attributes about these entities (c.g,, customer, product, time, circuit/driver), typically with row per entity/state, and a PK that is referenced by facts.
Graph perspective of RDB (Robinson et al., 2024). Each RDB and corresponding predictive lask can be viewed as temporal graph (--0071 (Vo-0.. E(- [(-oo, o, 1b, :v: fE), paired with task labels Y. Vi-oo.T) T) and T) are entities and links at time t S T. Φ maps each entity to its node type, e maps each link to its link type. fv and fE are the mappings of features.
Datasets and tasks. We consider a diverse set of datasets and tasks from recent works (Robinson et al. 2024 Wang et al.] 2024aa Chen et al. 2025b). Adopting the taxonomy from Robinson et al. (2024), we categorize these tasks into four types: entity classification, entity regression, recommen- dation, and autocomplete. Entity-level tasks (classification and regression) involve predicting entity properties at a given time fpreed- Recommendation tasks focus on ranking the relevance between pairs of entities at bpred- The autocomplete task involves predicting masked information in table columns. A comprehensive description of each dataset and task is available in Appendix B We illustrate the generation of an entity-level task in Figure
3.2 MODEL ARCHITECTURE DESIGN SPACE
Architecture choice. As shown in Table I to enable a fair comparison between DFS and RDL on RDB benchmarks, we construct compact, factorized design space for each family. RDL mod- els are built from three modules: (i) a structural-
encoder (partial labeling trick, learnable
embedding, or no augmentation) (Yuan et al.] 2024: Zhu et al.] (021), (ii) a message- pasggng net- work (PNA, HGT, SAGE, or ReIGNN) (Corso et al. 2020 et al. 20206 Robinson et al. 2024] Chen al. 2025a), and (iii) readout head (MLP, ContextGNN, or a shallow aggregator) Yuaan
itt al. 2024 Standard training hyperparameters such as learning rate, dropout, normalization, and neighbor fanout are also tuned. DFS methods are parameterized by three main knobs: the SQL-Ievel aggregation function (e.g., max/min/modd)), the number of aggregation layers (1-3 when supported), and the backbone model (TabPEN, LightGBM, or FT-Transformer) with batch size and hidden di- mension included as additional hyperparameters, Architectural components are explored via grid search, while other hyperparameters are sampled from a smaller space (see Appendix E.3
Design motivation. While it is not feasible to exhaustively cover all designs in graph machine learning (GML), our design space spans representative components. For message passing alone, this includes vanilla message passing, self-attention mechanisms, multi-aggregator schemes, and higher-order approaches. Importantly, classical GML architectures gain renewed significance in RDB tasks. For example, PNA, originally devised for molecular graphs, is well-suited for RDB tasks: its multi-aggreegation mechanisms naturally capture diverse feature interaction patterns, echo- ing the strengths of DFS bbsed approaches. In Appendix E.33 we provide more detailed description of each component and explain why some GML. designs are not suitable for RDB tasks.
Table 1: Search space of RDL and DFS-based methods for RDB tasks. Underline means these components will go over a grid search, while other components will be sampled.
Name
Architecture deságn space
Hyper-parameters
RDL
Structural feature
Message passing
Readont
Learning rate, dropaut, normalization, fanouz...
Laheling ID. Lemaable embedding, None PNA, HGT, Sage, ReiGNN
MI..P ContextGiNN, Shallow
Aggregation function
Aggregation layers
Backhone
Batch size. hidden dimension
Max, Min, Mode, ... (fixd))
1, 2, 3 (if possible)
TaNPEN, LightCiMM, Fi-Tansformer
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3.3 EMPIRICAL STUDY VARIOUS ARCHITECTURE DESIGNS
Evaluation setup. For entity-level tasks, we sample 15 configurations per architecture combination (180 per task). For recommendation, we sample 10 configurations. For DFS, the Robinson ct al. (2024) HPO utility with 20 trials per design (TabPFN requires no tuning). Following Robinson al. (2024), train up 20 epochs, capping each epoch 1,000 steps (recommendation) or Ba 2013) with an optional exponential LR scheduler. main focus, and the only efficiency constraint is that the model can fit single L40S GPU (48GB). See Appendix(E.6) [E.6foor more discussions on extending our pipelines to efficiency- aware scenarios. In this section, we only report entity-level results. The recommendation results presented in Appendix E.5 since the architecture choice there is less important. Our evaluation reveals the following key insights:
500 (entity-level), using Adam (Kingma
Efficiency
Observation 1. Efficacy and necessity of the design space. We first validate our proposed de- sign space by examining the best possible test performance. We compare our design space's performance with that of baseline models reported in the literature, including GraphS (Robin- son et al., 2024), ReIGNN (Chen et al. 2025a), ReIGT (Dwivedi al., 2025), KumoRFM (Fey et al. (225)), and ReILLM (Wu et al. 2025 on 17 Relbench (Robinson et al. (024) tasks. These strong baselines serve to highlight that our design space achieves competitive results. As shown in
Classification Bank
Regression Rank
Avergge fank noower Beteen
KumoftrM fine-tured)
Best (ours)
RDL (ours)
Rul-LLM
ReiGHN
DF5 (ours)
KumoRFM (te))
ReiaT
Graphsage
Griffin
KumoRrM (fine (fine-tuned) tuned)
Best (aurs)
RDL (ourg)
RelGw
Rei-LAA
KumoRFM (ie)
ReloT
DFS (ours)
Graphsage
Griffin
Figure 2: Performance comparison between the best configurations from our design space and base-
line models on entity-level tasks. "Best (ours)" means the better value of RDL and DFS. Full nu- merical results can be seen in Table 11 from Appendix E.5
Figure the best configurations from our design space consistently outperform all crratch-trained baselines. Specifically, we find the following two designs improving the performance of RDL and
DFS: (1) Breaking equivariance: learnable embeddings (e.g., partial labeling Zhu et al. 202
improve entity-level performance despite violating node-level permutation equivariance; (2) Better DFS implementation: compressing dense embeddings via incremental PCA and inserting them as numeric columns enables efficient cross-table aggregation in DFS (Wang et al.) 2025b), markedly improving scalability and accuracy. These performance results justify architecture selection: single architecture dominates across tasks. RDL vs. DFS rankings vary by task, and the same holds for micro-level choices (e.g., message passing; see Appendix E.5
Observation 2. Validation met- ries can be unreliable for architec- ture selection. Oracle test selection shows the upper bound of our design space, but in practice, configurations are picked by validation (or training) scores, which often leads to gap: validation-selected models underper- form test-selected ones (Table 2). In our reproduction of ReIGNN (Chen et al. 2025a), the reported gains over GraphSAGE are clear only when choosing hyperparameters by test performance; with validation selection, the advantage be- comes marginal. This reliability issue largely overlooked in graph AutoML and only briefly noted in tabular MI. (Ye al. 2024). It is pronounced in RDB settings, which are inductive and time-
Table 2: The performance gap between validation- and test- selected configurations.
Model
reported peff
lest-sclcted val-selected perí
driver-top3 (aurne)
GrapESAGE
75.54
82.81
81.56
ReIGNN
85.69
85.69
82.61
GraphSAGE
4,032
3.91
3.93
driver-postiie (snac
ReIGNN
user inerre (auroc)
Gra
ReIGNN
$1.62
86.18
86.40
86.18
78.94
72.27
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aware: temporal splits induce distribution shift between validation and test periods. The problem affects both RDL and DFS; further evidence is in Appendix E.5
PRINCIPLES AND AUTOMATION OF ARCHITECTURE SELECTION
Building on Section 3 where neither paradigm uniformly dominates, we seek a principled way to choose architectures for an RDB task. Two obstacles arise: (i) the design space is huge (180 trials per task cover only a small fraction), and (ii) validation performance-the the usual selection proxy-can be unreliable, so more search can even degrade test performance. We address this by leveraging the model performance bank (Section 3.3): for a new task, transfer information from similar tasks to reduce the search space. This requires a task embedding to capture the properties of tasks.
4.1 FROM OBSERVATIONS TO TASK EMBEDDINGS
We begin with two observations from the performance bank: (1) RDL-DFS performance gaps vary across tasks; and (2) validation-selectedd vs. test-selected performance gaps differ across model types. The first implies data factors driving task's affinity for certain model classes; the second motivates analyzing model properties to explain generalization.
4.1.1 DATA-CENTRIC PERSPECTIVE
We begin with homophily as the first axis of task characterization, since it is the lowest-order rela- tional signal and reflects task's favored inductive bias (Ma et al.) 2022). Label-induced properties empirically outperform lanel-ag ones (e.g.. degree) for performance prediction (Li et al.) 2023] Zheng et al., 2024) Mao et al., 2023). and labels are directly available in RDB tasks via the mate- rializing SQL query. Extending homophily to RDB tasks is non-trivial because: (1) labels evolve over time; (2) labels may be continuous; and (3) the PK-FK graph is schema-driven- labels usu- ally come from single fact table, so naively computing edge homophily on raw PK-FK links is ill-posed (always equal 1). We therefore propose RDB task homophily. Starting from the PK-FK graph used for training, temporally aggregate labels to per-entity means de E RC, then aug- ment the graph as in Figure to form self-looped metapaths; for scalability, we restrict to one-hop metapaths.
Definition (RDB task homophily). Given augmented heterogeneous graph G (V,E) induced from an RDB task, labeled entity type F and VF its nodes, each with mean label t.- Let M be a finite set of self-looped metapaths m starting and ending with F, and let &m be the set of edges induced by m. Given a label metric K., the RDB task homophily for metapath i is
H(G:m) = Em
Σ fu,
Klaa Bo).
Label metric design. For classification tasks, the label met- ric can be the dot product K(iuu B.) v. Dv. which reduces to traditional edge homophily 1 = Go when there are no duplicate entities in the task table. We denote this mea- sure by (atge m). For regression tasks, we instead use a correlation-base label metrie: letting Eu = (ū.. u)/a de- note standardized predictions over labeled nodes (with em- pirical mean 14 and variance d²), we define K(p b.)
DriverID
Results
Constructors
FK
RaceIb
PK
ConstructorID
FK
DriverID
Rooes
FK
ConstructorID
PK
ReceIb
Degenerate: homophily =]
Results
Drivers
Va Dv = (#==))((m-1) to measure Pearson-style correla- o² tion of labels along edges of each metapath. We may further extend the homophily definition to account for class imbal- ance. A notable extension is the adjusted homophily (Platonov et al. 2023). For classification task, it can be defined as D mm)
Poirs of FKs os edges: homophily c
Drivers
DriverID- RoceTD
Drivers
Orivers
brwentb- ConstructorID
Deivars
Figure 3:
Augmenting REL-FI
databases. We should treat the set of FKs hyperedge (each pair of FKs is appended the original PK-FK graphs as a new edge type) rather than relying solely on PK-FK -K edges.
Hetge(Smm) -C
Huai (G:
21
where ((m) is the
D 2iem
degree of class k. To obtain a global measure across the whole graph, aggregate over metapaths using statistical functions such as MEAN and STD.

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Anchor-based affinity properties. Homophily is most suited to node-level equivariant message- passing and overlooks path-based models with labeling tricks and non-structural factors (feature quality, temporal dynamics). We therefore add machor-based affinities to estimate which model families a task favors. If random path-based aggregation already separates labels well, path-based models should excel. See Appendix Coor the relationship between random hashing and randomly initialized NBFNet. Compared to task embeddings requiring backpropagation (Cao et al., 2023), these features only require a forward pass and closed-form fitting, which much more efficient.
1. Path/neighborhood affinity. Use randomly initialized GraphSAGE and NBFNet (Zhu et al., 2021) as random hashers; after one forward pass, fit a closed-form linear head (ridge or LDA). A random NBFNet achieves > 82 AUROC on USER-BADGE. indicating strong path affinity.
2. Feature affinity. Use TabPFN validation performance (no training) as a proxy for feature quality.
3. Temporal affinity. Since mean-label homophily ignores time, add simple timeline statistics (e.g., majority label over time), which effectively capture dynamies (Cornell et al. 2025).
Number of training rows. Among basic RDB statistics, the number of training rows Numin plays a uniquely important role. Unlike other dataset statistics (e.g., the number of test rows or table counts), Nuain directly determines the amount of context model can access during learning. This is especially critical for foundation model- -based approaches such as DFS+Tab where Nuain governs how much historical information the model can attend to in its context window. We therefore include (Nraan) as a dedicated feature alongside the affinity properties.
Correlating heuristics with the RDL-DES performance gap. We conduct a nonparametrie cor- relation analysis relating RDB task characteristics to the performance differential between the best RDL and DFS models. We identify that log (Nriin) representing the logarithm of training row counts, and adjusted homophily are the most significant predictors. Notably, in classification tasks, adjusted homophily displays a strong negative correlation with the RDL-DFS gap, with Spearman's = -0--0. 00.43 (p 0.05). (See Appendix C for complete heuristic definitions). This implies that RDL's nonlinear aggregation is particularly advantageous for low homophily tasks. Moreover, RDL requires substantial supervision signals to learn appropriate aggregation functions. We evaluate other heuristics, including the influence of labeling tricks and path affinity, in Appendix E.5
(Informal) Theoretical insights. The correlation between the relative performance of RDL and DFS, homophily-induces features, and the train task table size (number of available labels) can be elucidated from graph-theoretice perspective. If we formulate the RDB as a heterogeneous graph and expand into multi-relational graph via metapaths, both DFS and RDL can be viewed as mechanisms that aggregate neighbor information into score for each task-table row. However, DFS relies on fixed linear averages of neighbor signals, whereas RDL learns relation-specifer nonlinear transformations. (e.g.. amplification, clipping, or sign flips) prior to combination. We briefly discuss two regimes here and provide rigorous proofs in Appendix C.2
1. Low-homophily classification. When labels strongly homophilous along most metapaths, simple averaging is close to optimal and DFS already captures most relational signal. When some metapaths are weakly homophilous, heterophilous, or nearly random, linear averaging mixes pos- itive and negative evidence and tends to cancel signal. RDL can instead learn to down-weight uninformative relations and flip the contribution of systematically "opposite-label" metapaths, effectively increasing the signal-to-noise ratio exactly when adjusted homophily is small or neg- ative. This explains why the RDL-DFS gap grows on low-homophily classification tasks.
2. Dependence on the number of training rows. The same view clarifies why the number of training rows, Numin is a key moderator. DFS has a relatively small hypothesis class: aggregators are fixed and only a tabular model is learned, so its estimation error shrinks quickly with sample size. RDL introduces many additional parameters (per-relation weights, gates, nonlinearities) which in principle allow it to recover the "right" aggregation but also make it casier to overfit when supervision is scarce. In low-data regimes, these learned gates are noisy and can hurt performance compared to the stable DFS averages; once log( Nurai)) is large enough, the gates can be estimated reliably and RDL's extra flexibility turns into a consistent advantage.
4.1.2 MODEL-CENTRIC PERSPECTIVE
To understand validation-test selection gaps, we analyze checkpoints that exhibit good and poor generalization. After conducting intuitive visualization-baase analysis (shown in Appendix R.5).
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probe generalization via the local loss landscape L R + R around checkpoint Wy (Chiang al. 2023). This is also consistent with recent evidence that mode connectivity in GNNs is informative for understanding optimization geometry and model robustness (Li 2025). Fix an orthonormal 2DD subspace = span(e) (e1, e2) and sample a grid r = {(s, 45} C Tps
-P p| Each grid point
defines Waj wi + 8;61 tg0g with Tuj [(was) We summarize with three indicators spanning
increasing smoothness scales (Garipov et al. 2018 Li et al., 201 Ghorbani et al.) 2019):
1. First-order P1: + V(s.-su)*++00
[T.. Teal
(worst miite-difffeeene slope on II), de-
tecting brittle checkpoints near loss cliffs.
2. Second-order P:: ( (wo)), where Hn(wo) = E (w)) E = [e] egl (sharpness along II), measuring basin curvature; high P2 signals narrow minima sensitive to distribution shift et al.. 2018a).
3. Energy barrier Pbart max((,)) max L(wo + (wwi - wo)) - max[L(wa)) Lij} (barrier to departing wo along rays within IT), probing global basin geometry rather than local curvature.
These three metries span local slope, curvature, and global basin shape, and can occasionally con- flict (e.g., moderate Pi but high P3 when the surface curves steeply beyond the immediate neighbor- hood). On DRIVER- TOP3 (Table 3 when validation-test gaps are large all indicators consistently favor the test-optimal checkpoint; when gaps small they may disagree, motivating their joint use. These metrics are comparable within a model family (RDL or DFS) but not across families due to scale differences, and are effective only for well-fitter models. Since these signals are post-hoc, use them to refine the final checkpoint choice among top-validation candidates.
4.1.3 AUTOMATIC ARCHITECTURE SELECTION THROUGH RELATRON
Based on these findings, we intro- duce Relatron, architecture lector that maps task embeddings to meta-predictions about which model design to use. Given an RDB task, Relatron considers two types of chitecture selection.
Table 3: Example landscape properties and model performance.
Smaller values typically indicate more benign landscape.
Selection
Model type
Val auroe
Test muroc
Plar
P1.
13
Val
RDL
89.48
82,41
2.77
1.49
4.23
Test
85,94
1.22
0.22
Val
DFS
83.44
84,69
0.384
0.041
1.32
Test
DFS
83.76
85.71
0.495
0.03
0.50
Macro-level selection (RDL vs.
DFS). We train meta-classifier on the performance bank to map task embeddings to the empir- task embeddings. At inference, we (1) compute the novel task's 's embedding and (2) apply the meta-classifier to choose between RDL. and DFS. Joint architecture selection and HPO with a query budget. For standard HPO with a query- budget setting, the query budget is appended to the task embedding A macro-level meta-predictor first chooses between RDL and DFS. (2) An HPO routine (e.g..
ically winning family, using homophily-bard
additional feature. (1)
TPE (Bergstra et al.] 2011) Autotransfer (Cao et al., (023)) generates candidate checkpoints within the selected family. (3) Loss-landscape metrics are applied for post-selection among candi- dates with top validation performance. An insight here is that the favored model type is related the query budget. Although RDL often attains higher best-case performance on tasks such as STUDY- OUTCOME, under tight search budgets, its average performance can lag behind DFS because good RDL configurations are harder to find. Moreover, as shown in Section 4.2, we surprisingly find that the macro-level meta-predictor (DFS or RDL) addresses most issues: after selecting the appropriate model branch, search efficiency improves and the validation- test gap narrows.
Design space of task features. In terms of task feature design, there are three categories, with com- putation budgets ranging from small to large. A detailed introduction can be found in Appendix F
1. Model-free embeddings: Model-free embedding requires no training and is extremely fast to compute. This includes homophily-based features, the performance of simple heuristic baselines, basic database statistics, and temporal-related correlations.
2. Training-fres Model-based embeddings: These embeddings use training-free models' perfor- mance as embeddings. This includes the performance of DFS-TabPFN and that randomly initialized GraphSAGE or NBFNet.
3. Anchor-based embeddings (Cao et al., 2023): This refers to task2vec (Achille et al. 2019 based anchor model-based embeddings, such as Auto-transfer. Though the original paper claims
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Table 4: Experimental results for embeddinn similarity, leave-ore oeee-ooe-uut (LOO) ac-
curacy, and task museemnddding computation efficiency. "AT" stands for Autotransfer. For "winner by val," we still report test performance, but the representative checkpoints are selected by validation performance.
Task emnbrrding design
Mier Kendall's Maan Kendall's (g)
Winner val
Winner
time (min)
Medil free chorapphily tomppes))
B8666
0.163
87.5%
76.25
0.88
Muckl Giiple Anchor (Astooansoo))
70.8%
75.0%
Figure 4: Ground truth GraphGym similarity
66.7%
that these embeddings require little time to obtain, we find that computing the Fisher information matrix is actually very consuming for RDB tasks, given the number of required anchors.
4.2 EXPERIMENTAL EVALUATIONS
We then evaluate the proposed Relatron on three experiments. (1) samity check of task embeddings: First, we evaluate the effectiveness of different task embeddings by comparing the task similarity calculated by task embedding and the ground truth Graphgym similarity (You et al. 2020). This ex- periment is mainly used to verify the correctness of task embeddings. (2) Macro-level architecture search: Second, we check whether task embeddings can help identify the proper architecture for an RDB task. (3) Joint selection of architectures and hyperparometers: Third, we consider a more prac- tical scenario, in which task embeddings and meta- peea-pre are used to enhance the effectiveness of joint hyperparameter and architecture search.
Can task embeddings reflect ground-truth task similarity? Using all trials in the performance bank, we: (1) derive ground truth GraphGym similarity (You et al., 2020) by intersecting model configurations across tasks, ranking them by per-task performance, and defining pairwise similar- ity as Kendall's 7 between the two rank signatures. As a sanity check, the top 3 similar tasks are USER-BADGE, USER-ENC BEERR-ENAAGEEENT and USER-CHURN, which aligns with the similarity of their homophily metrics. (2) for each embedding, form a task-task matrix via cosine similarity on nor- malized features and report Kendall's rank correlation with the GraphGym matrix; (3) optionally learn a projection (as in Cao et al. (2023) using a margin-ranking meta-objective that pulls to- gether tasks with similar performance profiles and pushes apart dissimilar ones. Results (Table 4 Our proposed homophily and affinity- embedding achieves the best ranking correlation. On the other hand, we also need to point out that none of these task embeddings present significantly high correlation, which can partially explain why transfer-based HPO is not very effective in Table
Can task embeddings help predict RDL vs. DFS winner? We then investi- gate whether task embeddings can predict which meLhod-RDL or DFS Drr-performs better on a novel task. We consider win- ners selected by validation performance and directly extracted using test perfor-
Table 5: Ablation performance-bant size for macro-level predictors (LOO accuracy).
Bank Model-free (ours)
Training- free model-based
Anchor-based
68, 9%.9%
56.9%
62.9%
12
64.8%
50.4%
56.1%
16
76. 76.25%
All
66.7%
66.7%
mance. The former one is exactly the setting for architecture selection during the HPO. For each target task, we fit the model using other tasks in the model performance bank and evaluate the tar- get task with leave-one-out features are surprisingly most effective despite the low computation cost. If we further look at the incorrect samples, we find that most of them have small performance gap between RDL and DFS (within 2.5%), indicating that these tasks are inherently hard to distinguish. Moreover, shown in Table we also study the influence of model performance bank size. As expected, larger banks lead to better predictors since they cover more diverse tasks. At the same time, our proposed model-free task features consistently outperform other embeddings under different bank sizes.
vols-vaaliatinn. As shown in Table 6 our proposed model-free task
Joint hyperparameter and architecture search. We evaluate our full pipeline in a joint HiPO-archiecttre-saacch setting. As baselines for trial generation, we use random search, TPE (Bergstra et al. 2011). Hyperband (Li et al.) 2018b), and Autotransfer (Cao et al. 2023). We report results on three representative datasets DRIVER-TOP? CHuRN-and use the remaining tasks as the performance bank; the first two have small training tables and are prone to overfitting. Our pipeline trains a MabPFN-baseed meta- preai-rredctto that selects between RDL. and DFS from the query budget and homophily-based task embeddings. Conditional
PELVVER-,OITIO,, and USER-
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Table 6: Joint architecture and hyperparameter optimization result. Best results are highlighted with an underline and the second are bold. "OOly predictor" means only using the meta-predictor Relbench's 's default refers to the default architecture and hyperparameters in their pipelines. Best fixed refers to the configurations with the best mean rank of performance across tasks.
Strategy
Budget
driver-top3 (ROC-AUC) /
driver-position (MAE) >
user-churn (ROC-UUC)
3
$2.67 2.19
3,6810 = 0,4255
68,56 1.23
Random
10
77.80 4.79
3,8576 0.5035
68.60 1.20
30
77.28 2.42
4,2793 a 0.1483
69.54 a: 0.43
$2.67
3.6810 0,4255
1.23
TPB
10
81.45 + 0,44
3,7897 0,4271
68,60 1.20
30
77.92 + 5.12
4.1724 = 0,0519
69.54 2 0.43
82.67
3.6810 0,4255
1.33
Hyperband
10
80.68 + 0.74
3.7897 0,4271
68, 60 1.20
30
74.37 = 9.59
4,0948 = 0.1420
69.32 0.34
4.43
4.2916 0.0952
Autotransfer
10
78.71 2.88
4.3645 + 0.2105
20.28 + 0.27
Only predictor
Full
Only predictor
Full
Only predictor
Fall
83 0.34
NA
3.3986 0.0877
NA
68.78 + 1.31
NA
Ours
10
83.28 + 1.45
83.30 1.17
3.3934 0.1389
3.3553 * 0.0862
69 06 + 0.89
69.61 + 0.86
30
84.00 + 0.34
84.33 + 0.06
3,3339 0.1563
3,3339 + 1563
70.05 + 0.34
70.05 0.34
+ TabPFN
82,24
3.43
67.79
Relbench's 's default
73.19
5.02
68.12
fixed
83,72
4.35
Griftin
77.95
4,20
68.4
on this choice, we run TPE- the best-performing trial generator-witee the selected family, then post-select among the top-3 validation models using landscape measures.
As shown in Table we observe: (1) our pipeline generally achieves better performance. Us- ing only the peta-predictor to choose the model family already yields strong results, suggesting that large validation-test gaps often arise from selecting an unsuitable architecture. Unlike other methods that suffer from the undesirable "the more you train, the worse you get" phenomenon, our perfor- mance continues to improve as the number of trials increases. Post-hoo bost-hoc selection offers only limited gains, likely because hard voting numeric landscape metrics still introduces noise. Addressing checkpoint selection may require pre-trained priors similar to architecture search; we leave this to future work. (2) For search acceleration, unlike Cao et al. (202)), embedding- bassed configuration retrieval is typically ineffective, with the sole exception of USER-CHURN Consequently, we rely on TPE for trial generation. The limited effectiveness of Autotransfer suggests that RDB data distri- butions are more complex than those of standard graph benchmarks, and that a larger, more diverse task bank would be needed -impactticll given the scareity of public data. Synthetic tasks, in the spirit of Hollmann et al.) (2023), may therefore be promising direction to improve search quality. (3) After hyper- parameter tuning, we demonstrate that models trained from scratch can outperform foundation models like DFS with TabPFN.
CONCLUSION, LIMITATIONS, AND FUTURE DISCUSSION
In this paper, we systematically study the design space of relational machine learning models for RDB tasks and collect a model performance bank. Based on this study, we show that the advantage of RDL over DFS is related to task properties, like the RDB task homophily. Then, we propose a meta-predictor based on the model performance bank and our proposed selector, Relatron, which demonstrates promising performance in both macro- and micro-level architecture search.
Limitations and future work. Our study does not explore LL-M-based methods, either as en- coders (Wang et al., (025b) or predictors (Wu et al., 2025). While these approaches excel on certain databases, they often perform similarly or worse than baselines on other databases, leaving their role an open question. Although we do not propose new architectures, our results highlight design insights: GNNs with labeling tricks can boost entity-level prediction, and ten outperform RDL. suggesting that current RDL designs may be suboptimal. Yet DFS remains a non parammeet,,,,, hand-crafted approach, contrasting with deep learning trends. Designing novel architectures inspired by DFS thus represents promising direction.
methods of-
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ETHICS STATEMENT
We acknowledge that we and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics. Our study relies solely on publicly available benchmark datasets such as Relbench (Robinson al. 2024). We believe this work raises no direct ethical risks beyond standard concerns associated with machine learning research.
7 REPRODUCIBILITY STATEMENT
We provide training settings Section 3.3 Section 4 and Appendix E in this paper, we include theoretical discussions in Appendix
To validate the observations
REFERENCES
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Char- less C. Fowlkes, Stefano Soatto, and Pietro Perona. Task2vec: Task embedding for meta- learning. In 2019 IEEE/CVF International Conference on Computer Vision. ICCV 2019, Seoul, Korea (South). October 27 November 2. 2019, pp. 6429-6438 IEEE, 2019. doi: N0.1109//CCV22219000006 URL
2010 00653
Jinze Bai, Jialin Wang, Zhao Li, Donghui Ding, Ji Zhang, and Jun Gao. Atj-net: Auto-table-join network for automatic learning relational databases. In Jure Leskovec, Marko Grobelnik, Mare Najork, Jie Tang, and Leila Zia (eds.), WWW 21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, pp. 1540-1551. ACM IW3C2, 2021. doi: 10.1145/ 1115/11444333 220.22311344999 3449980
3442381 3449980. URL /ttt::::/dooi.croo
Maya Bechler- Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Sir- audin, Viktor Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, and Christopher Morris. Position: Graph learning will lose relevance due to poor benchmarks. In Forty-second International Conference Machine Learning Position Paper Track, 2025. URL
httts:://ppereviiee http://awnrreeeeeeeeeooooooaaaap httt///ooommiieaDppllleeee.
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper- parameter optimization. In John Shawe Taylor. Richard S. Zemel, Peter L. Bartlett, Fer- nando C. N. Pereira, and Kilian Q. Weinberger (eds.). Advances in Neural Information Processing Systems 24: 25th Annual Conference Neural Information Processing Sys- tems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain, pp- 2546 2554, 2011. URL pttts:::/ppoceeddgss..eeee 7 2TD.3/CCC22177722266666666353539 Abstssaobbbbattct..... html
Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Mare Becker, Anne-Laure Boulesteix, et al. Hyperparameter opti- mization: Foundations, algorithms, best practices, and open challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2)=e144, 2023.
Kaidi Cao, Jiaxuan You, Jiaju Liu, and Jure Leskovec. Autotransfer: Automl with knowledge transfer- an application to graph neural networks. The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023.
URL httpp:://ppeneeiiiiet new.:/ww.eeeeeeeee..eee.ee htt.ap/weeeee..eee///o/eaoaaaaaa vg
Tianlang Chen, Charilaos Kanatsoulis, and Jure Leskovec. ReIGNN: Composite message passing for relational deep learning. In Forry-second International Conference Machine Learning,
2025a. URL htttt/s::///peeeeeiiii/te
net itw.
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, and Jiliang Tang. Exploring the potential of large language models (Imss in learning on graphs SIGKDD Explorations, 25(2)42-61 2023, doi: 10.1145/3655103, 3655110. 10. URL httphttts: httt::/:/ooo.oooo/..1155////5//. 10t./11..11//555111..33333

---

Published as a conference paper at ICLR 2026
Zhikai Chen, Han Xie, Jian Zhang, Xiang song, Jiliang Tang, Huzefa Rangwala, and George Karypis. Autog: Towards automatic graph construction from tabular data. In The Thirteenth In- ternational Conference on Learning Representations, 2025b. htttss://ppeneeeeee
dehovDbb44Gh
Ping-yeh Chiang, Renkun Ni, David Yu Miller, Arpit Bansal, Jonas Geiping, Micah Goldblum, and Tom Goldstein. Loss landscapes are all you need: Neural network generalization can be explained without the implicit bias of gradient descent. In The Eleventh International Conference on Learn- ing Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, OpenReview.net, 2023. URL https: httpp::///pperevieww/e net peet/pdffiie QC1 0RmRbZy
Dongwon Choi, Sunwoo Kim, Juyeon Kim, Kyungho Kim, Geon Lee. Shinhwan Kang, Myungh- wan Kim, and Kijung Shin. RDB2g-bench: A comprehensive benchmark for automatic graph modeling of relational databases. arXiv, 2025. doi: 10.445500ammix22500001333
Edgar F Codd. Relational database: A practical foundation for productivity. In ACM Turing award lectures, pp- 1981. Association for Computing Machinery, 2007.
Filip Cornell, Oleg Smirnov, Gabriela Zarzar Gandler, and Lele Cao. On the power of heuristics in temporal graphs. ArXiv preprint, abs/2502.04910, 2025. URL
25C2. 04910
Gabriele Corso. Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Velickovic. Principal neighbourhood aggregation for graph nets. In Hugo Larochelle, Mare' Aurelio Ranzato, Raia Had- sell, Maria-Floring Balcan, and Hsuan-Tier Lin (eds.), Advances in Neural Information Process- ing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurlPS 2020, December 62, 2020, virtual, 2020, URL 10ppe//200//aah/999a2222 /at.a//02222222.2222220000000000
wwwripeu.rc..oo cc/
Abstract.
DMDave, Todd B, and Will Cukierski. Acquire valued shoppers challenge, 2014. URL https:
//kaggle
Vijay Prakash Dwivedi, Sri Jaladi, Yangyi Shen, Federico López, Charilaos I Kanatsoulis, Rishi
Puri, Matthias Fey, and Jure Leskovec. Relational graph transformer. arXiv, 2025.
Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, and Jure Leskovec. Position: Relational deep learning graph rep- resentation learning on relational databases. In Forty-first International Conference on Ma- chine Learning. ICML 2024, Vienna, Austria, July 21-27 2024. OpenReview.net, 2024. URL
https: htttps://openreview openreview.eeeeeeeoeeee
Matthias Fey, Vid Kocijan, Federico Lopez, Jan Eric Lenssen, and Jure Leskovec. Kumorfm: A foundation model for in-context learning on relational data. kumorfm.ai, 2025.
Yang Gao, Hong Yang. Peng Zhang, Chuan Zhou, and Yue Hu. Graphnas: Graph neural architecture search with reinforcement learning. ArXiv preprint, abs/1904.09981, 2019. URL https://
abs
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, and Andrew Gordon Wil- son. Loss surfaces, mode connectivity, and fast ensembling of dnns. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett (eds.), Advances in Neural Information Processing Systems 31: Anmual Conference on Neural Infor- mation Processing Systems 2018, NeurlPS 2018, December 3-8, 2018, Montréal, Canada, pp. 8803 8812, 2018. URL
Behrooz Ghorbani, Shankar Krishnan, and Ying Xiao. An investigation into neural net opti- mization via hessian eigenvalue density. In Kamalika Chaudhuri and Ruslan Salakhutdinov (eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9- 15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pp. 2232-2222222 2241. PMLR, 2019. URL http: httt:://proceecings.o mlr. wtt.sp.ss.s///
ghorbani 1 9b. html
12

---

Published as a conference paper at ICLR 2026
Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, and Wenwu Zhu. Large-scale graph neural architecture search. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song. Csaba Szepesvári, Gang Niu, and Sivan Sabato (eds.), International Conference July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Re- search, pp. 7968-7983 PMLR, 2022. A https: http://pooceeedee ngs. mog..mlr.. o preas/v162/
Machine Learning, ICML 2022, 17-23
guan22d.hta html
William L. Hamilton, Zhitao Ying, and Jure Leskovee. Inductive representation learning on large graphs. In Isabelle Guyon, Ulrike von Luxburg. Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (eds.), Advances in Neural Information Processing Systems 30: Annual Conference
Neural Infor- mation Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp. 1034-1034, 2017. URL http::::/p:::peeeeeeeeee
www.ies..p..... http:/appr/2211//aee - html
Jan Harrington. Relational database design and implementation. Morgan Kaufmann, 2016.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pp. 770-778. IEEE Computer Society, 2016. doi: N0.1109/CVPR.2016.990 BRLhhtpps: htt.::::/ddo..org//// 1wtp:1111..11122 2016.
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter. Tabpfn: trans- former that solves small tabular classification problems in a second. In The Eleventh Interna- tional Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. http::/
wwt. http:/dddddddpppeeeee 6w8_
Addison Howard, Bernadette Bouchon-Meunte IEEE CIS, inversion, John Lei, Lynn Vesta, Marcus2010, and Prof. Hussein Abbass. Icce-cis fraud detection. https: /Ettps//kaggle... kaggle..gg.... com/ competitionss
fraud
2019. Kaggle.
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. In Hugo Larochelle, Marc' Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (eds.), Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurlPS 2020, December 6-
12, 2020, virtual, 2020a. URL attps: http::::s//rrocedinng. wtw.:oeeeeddnn...nui........... www.ine..ipp... wwwperrips.cpppaaaeeee http:accccppppppppeeeeeeeeeeee pctpaper/202c/ hash/
2ttei/22222222222222 7w3327 www.aadd-Abstratt...m .html
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. Heterogeneous graph transformer. In Yennun Huang, Irwin King, Tie-Yan Liu, and Maarten van Steen (eds.), WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, pp. 2704 2710. ACM / IW3C2, 2020b. doi: 10. 00.1145/336642333380000
httpttps:: doi org 100 1145 /3/44//333666 336642336333333333 3380027|
Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren. Automated machine learning: methods, sys- tems, challenges. Springer Nature, 2019.
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, freely accessible critical care database. Scientific data, 3(1):1-9, 2016.
James Max Kanter and Kalyan Veeramachaneni. Deep feature synthesis: Towards automating data science endeavors. In 201510 IEEE international conference on data science and advanced analyt- ics (DSAA), pp. 1-10. IEEE, 2015.
Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun (cds.). 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. 2015. URL http:
7/arxiv. wra.ivoorrraaaa abs a1441//ab/1111... 412. 6980
Thomas N. Kipf and Max Welling. Semi supervsuer classification with graph convolutional net- works. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 26- 2017, Conference Track Proceedings. OpenReview.net, 2017. URLhetts. //openrevien ew. net. fwrmm d
13

---

Published as a conference paper at ICLR 2026
Hoang Thanh Lam, Johann-Michhrl Thiebaut, Mathieu Sinn, Bei Chen, Tiep Mai, and Oznur Alkan. One button machine for automating feature engineering relational databases. ArXiv preprint, 00s/1706.003330 2017. URL /tRtttt:://axxii//
haagg/ab//110 00327
Hoang Thanh Lam, Tran Ngoc Minh, Mathieu Sinn, Beat Buesser, and Martin Wistuba. Neural feature learning from relational datahase. ArXiv preprint, abs/1801.05311 2018. URL https:
// //a.iv..rgg/aaa...... abs 11001. 05372
Hoang Thanh Lam, Beat Buesser, Hong Min, Tran Ngoc Minh, Martin Wistuba, Udayan Khurana, Gregory Bramble, Theodoros Salonidis, Dakuo Wang, and Horst Samulowitz. Automated data science for relational data. In 2021 IEEE 37th International Conference on Data Engineering doi: D0.1109/CCD311399.00 1.0005..
(ICDE), pp. 2689-2692, 2021.
Bingheng Li, Zhikai Chen, Haoyu Han, Shenglai Zeng, Jingzhe Liu, and Jiliang Tang. Unveiling mode connectivity in graph neural network. In Proceedings the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2, pp. 1308-1317, 2025.
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. Visualizing the loss landscape of neural nets. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Gamett (eds.), Advances in Neu- ral Information Processing Systems 31: Annual Conference on Neural Information Pro- cessing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pp. 3a. URL /pLptttts nee. CC pappr/2018/haa.. html
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. Hyperband: A novel bandit-based approach to hyperparameter optimization. Journal of Machine Learning Research. 18(185):1552, 2018b.
Ting Wei Li, Qiaozhu Mei. and Jiaqi Ma. A metadata-driven approach to derstand graph neural networks In Alice Oh, Tristan Naumann, Amir Glober- son, Kate Saenko, Moritz Hardt, and Sergey Levine (eds.), Advances in Neural In- formation Processing Systems 36: Annual Conference on Neural Information Pro- cessing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 16, 2023, 2023, URL 03340010ee222b55000040000-----00
Yaoman Li and Irwin King, Autograph: Automated graph neural network. In Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part 11 27, pp. 189-2001 Springer, 2020.
Junhong Lin, Xiaojie Guo, Shuaicheng Zhang. Dawei Zhou, Yada Zhu, and Julian Shun. When heterophily meets heterogeneity: New graph benchmarks and effective methods. ArXiv preprint, Abs/2407.10016 2024. URL htt.ihtts::///
org abs 10916
Yuankai Luo, Lei Shi, and Xiao-Ming Wu. Classic gnns are strong baselines: Reassess- ing gnns for node classification. In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomezak, and Cheng Zhang (eds.), Advances in Nearal Information Processing Systems 38: Annual Conference on Neural Informa- tion Processing Systems 2024, NeurIPS 2024. Vancouver, BC, Canada, December 10 15, 2024, 2024. URL ctc wwwps.cppppppp.....ppccppppppppe forrr_fiiee
Track.html
Zhipeng Luo, Zhixing He, Jin Wang, Manqing Dong, Jianqiang Huang. Mingjian Chen, and Bo- hang Zheng. Autosmart: An efficient and automatic machine learning framework for temporal relational data. Feida Zhu, Beng Chin Ooi, and Chunyan Miao (eds.), KDD '21: The 27th ACM SIGKDD Conference
Knowledge Discovery and Data Mining, Virnual Event, Singa- pore, August 14-18, 2021, pp. 3976 3984, ACM, 2021. doi: 10.1145/3447588.4660 3467088 URL 01011114444444 3467088888444888
https://doi .rg. wtt.g/....11//441111
14

---

Published as a conference paper at ICLR 2026
Weishuo Ma, Yanbo Wang, Xiyuan Wang, and Muhan Zhang. Reconsidering the performance of gae in link prediction. ArXiv preprint, abs/2411.03845,, 2024. URL https:: /ttl:t:////axii.... http:///xxxvv..o////
abs abss2411.. 03845)
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang. Is homophily necessity for graph neural networks? In The Tenth International Conference on Learning Representatiins, ICLR 2022, Virtual Event, 25r2 25-29, 2022. OpenReview.net, 2022. UtLettpps://ppeneevieer Forum? Cerum?id-uchen SeerumidducASPPDDGGI
Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, and Jiliang Tang. Demystifying structural disparity in graph neural networks: Can one size all? Advances in Neural Information Processing Systems 36: Annual Conference Neural Information Pro- cessing Systems 2023, 2023, URL / Saieerr__illppppppe 2023
nips ctcppp..cccccc wwtppppppppppppppppppppppppppp.p
ht.l
Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail
Galkin, and Jiliang Tang. Position:
Graph foundation models are already here. In Forty-
first International Conference on Machine Learning. pp. 34670-34692, 2024. URL https: html
//proceedings.. .tree...s//ss.s.seee.e..ee halsss//225mmmmm
Darnelle Melvin. Relational database schema to support research profiling studies, natural language processing, and bibliometric analysis. UNLV, 2025.
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. Tudataset: A collection of benchmark datasets for learning with graphs. In JCML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020), 2020. URL www. graph eernnng.nnng.i
Namyong Park, Ryan A. Rossi, Nesreen K. Ahmed, and Christos Faloutsos. Metagl: Evaluation- free selection of graph learning models via meta-learning. In The Eleventh International Confer- ence on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023a. URL /nnttps://opeee http::://pppeeeeeeeeeeeeee net wid=:fdd=c1n0@pppp http://wwwweeeeedddddd/////aaaaa
Namyong Park, Ryan A. Rossi, Xing Wang, Antoine Simoulin, Nesreen K. Ahmed, and Chris- tos Faloutsos. GLEMOS: benchmark for instantaneous graph learning model selection. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko. Moritz Hardt, and Sergey Levine (eds.), Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurlPS 2023, New Orleans, IA, USA, December 10 16, 2023, 2023b. URL httt:://papers.nttt nips wwwppper.pppp.....eepee files www.s//wapeeeeeeee./.eaea pappr/2023///////////p hash/ Ber.charkks.o html
-baaar-bstttttt Datasets and
Oleg Platonov, Denis Kuznedelev, Artem Babenko, and Liudmila Prokhorenkova. Characteriz- ing graph datasets for node classification: Homophily-hetrrophiyy dichotomy and beyond. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (eds.), Advances in Neural Information Processing Systems 36: Annual Conference Neural Information Processing Systems 2023, NewrIPS 2023, New Orleans, LA, USA, December 10 16, 2023, 2023. 011681025
nips cc/paper http:/papr__fleeeeeeeeeeeeeeeeee fft.es//paperr/222/aa.... Sotereeeeeeeetttttttttooooeeeeee
Yijian Qin, Xin Wang, Zeyang Zhang, and Wenwu Zhu. Graph differentiable archi- tecture search with structure learning. In Mare' Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang and Jennifer Wortman Vaughan (eds.), Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NewrIPS 2021, December 6-14, 2021, virtual, pp. 16860- htc//aaper/222///a Abstract html
16872, 2021. URL https: htttt:::/pproceeiiie www.eeedinnn.....iii...........e Sc9f32e03aebee 3e 2a0ee022222000002288888888888222 wtwp://cccdd---ssaaattttte
Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, and Zachary Lipton. Post-hoc reversal: Are we selecting models prematurely? Advances in Neural Information Processing Systems, 37:91460-91199 2024.
15

---

Published as a conference paper at ICLR 2026
Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan Eric Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, and Jure Leskovec. Relbench: A benchmark for deep learning relational databases. In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, UIrich Paquet, Jakuh M. Tomezak, and Cheng Zhang (eds.), Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurlPS 2024, Vancouver, BC, Canada, De- cember 10 15, 2024, 2024, URL http: httttt::/pappr.....e nips cc wtwppppppppppppeepei.ee wttppaappleeeepppe_plappp httpa://2c3333333555ccc..eeeee00
and Benchmarks Track. html
Ion-Sorin Stroe. Mysql databases as part of the online business, using a platform based on linux.
Database Syst. J, 2(3):3 2011.
Minjie Wang. Quan Gan, David Wipf, Zheng Zhang. Christos Faloutsos, Weinan Zhang, Muhan Zhang, Zhenkun Cai, Jiahang Li, Zunyao Mao, Yakun Song, Jianheng Tang, Yanlin Zhang, Guang Yang, Chuan Lei, Xiao Qin, Ning Li, Han Zhang, Yanbo Wang, and Zizhao Zhang. 4dbinfer: A 4d benchmarking toolbox for graph-centric predictive modeling on rdbs. In Amir Globersons, Lester Mackey, Danielle Belgrave, Angela Fan, Ulrich Paquet, Jakub M. Tomczak, and Cheng Zhang (eds.), Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, Decem- ber 10 15, 2024, 2024a. /Ttp://4444000001151333303300300 -atp::777aabb----aaaaatttt----- Datasets
URL /ttt:://papete nips cc/ Files/paper/ Abst
and Benchmarks, Track. ht.m
Shijie Wang. Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Wenqi Fan, Hui Liu, Xiaorui Liu, Dawei Yin, and Qing L.. Graph machine learning in the era of large language models (Ilms). ACM Transactions on Intelligent Systems and Technology, 16:5):104:11104:44 URL //tteps:/ddor /ww./ http:///oooooooooooooooo/o/ooo.. 114511...111331211111
2025a. doi: 10.1 14114477737886
Xiyuan Wang, Haotong Yang, and Muhan Zhang. Neural common neighbor with completion for link prediction. In The Twelfth International Conference on Learning Representations, ICIR 2024, Vienna, Austria, May 7-11, 2024. OpenReview.net, 2024b. URL htttps:///ppeeeeeeeeee
net peet/Eormm?ccm sNF LN3itAd
Yanbo Wang, Xiyuan Wang. Quan Gan, Minjie Wang, Qibin Yang, David Wipf, and Muhan Zhang. Griffin: Towards a graph-centric relational database foundation model. In Forty-second In- ternational Conference on Machine Learning, 2025b. URL
forum?id-TxecxiD33
Rongzhe Wei, Haoteng Yin, Junteng Jia, Austin R. Benson, and Pan Li. Understanding non- linearity in graph neural networks from the bayesian-inference perspective. In Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (eds.), Advances in Neu- ral Information Processing Systems 35: Annual Conference on Neural Information Process- ing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 December 9, 2022, 2022. URL http: htat:::/papeer.mmtt httpppp.cccppppee.eeeeee/////2//
Jacob White. Pubmed 2.0. Medical reference services quarterly, 39(4):32-3377
Fang Wu, Vijay Prakash Dwivedi, and Jure Leskovee. Large language models are good relational learners. arXiv, 2025. doi: 200555/0aaxx.2200.05777777
Marek Wydmuch, Lukasz Borchmann, and Filip Graliáski. Tackling prediction tasks in relational databases with L.MMs. arXiv, 2024. doi: 10.48550/arxiv.2411 11829..
Han-Jia Ye, Si- Yang Liu, Hao-Rum Cai, Qi-Le Zhou, and De-Chuan Zhan. A closer look at deep learning methods on tabular datasets. ArXiv preprint, abs/2407.00956, 2024. URL https:
//arxiv. w...vvrg//.o www.orrgabboo haag///b//// /2007 220pb//0/00000066666
Minji Yoon, Théophile Gervet, Bryan Hooi, and Christos Faloutsos. Autonomous graph mining algorithm search with best speed/accuracy trade-off. In 2020 IEEE International Conference on Data Mining (ICDM), pp. 751-760. IEFE, 2020.
16

---

Published as a conference paper at ICLR 2026
Jiaxuan You, Zhitao Ying, and Jure Leskovec. Design space for graph neural networks. In Hugo Larochelle, Mare' Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan- Tien Lin (eds.), Advances in Neural Information Processing Systems 33: Annual Con- ference on Neural Information Processing Systems 2020, NeurlPS 2020, December 6-12, 2020, virtual, 2020. httL/httts:///rroeeeeet ngs. neurneuri..ess....m cct/paper/222//haaii c5ccaale622cc333//eeeealla.ccce2
-asttaattttttt.aae...... html
Yiwen Yuan, Zecheng Zhang, Xinwei He, Akihiro Nitta, Weihua Hu, Dong Wang. Manan Shah, Shenyang Huang. Blaž Stojanovič, Alan Krumholz, Jan Eric Lenssen, Jure Leskovee, and Matthias Fey. ContextGNN: Beyond two-tower recommendation systems. arXiv, 2024. doi:
10.48550/nxiw.2411115555
Muhan Zhang and Yixin Chen. Link prediction based on graph neural networks. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett (eds.), Advances in Neural Information Processing Systems 31: Annual Conference Neural In- formation Processing Systems 2018, NeurlPS 2018, December 3-8, 2018, Montréal, Canada, pp. 5171-5181, 2018 URL https: /ttp:::/e///eeeeeeeeeeeee otg.oe/ooooooooo//o/ooo.....oo..
13300001557233222220000222222223 neur ips
Zeyang Zhang, Ziwei Zhang. Xin Wang. Yijian Qin, Zhou Qin, and Wenwu Zhu. Dynamic hetero- geneous graph attention neural architecture search. In Brian Williams, Yiling Chen, and Jennifer Neville (eds.), Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Sym- posium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, pp. 11307-11315. AAAI Press, 2023. doi: 10.1609/AAALV337992533 1ttp://1006600//aeee vv7. 26338
URL
Wenqian Zhao, Xiangxiang Li, Guoping Rong, Mufeng Lin, Chen Lin, and Yifan Yang. Dafee: A scalable distributed automatic feature engineering algorithm for relational datasets. In Algorithms and Architectures for Parallel Processing: 20th International Conference, ICA3PP 2020, New York City, NY, USA, October 2-4, 2020, Proceedings, Part II. pp. 32-46, Berlin, Heidelberg, 2020. Springer-Vette Verlag. ISBN 978- 000---0000------3. doi:
00077799788--03-00222-49 URL https: //tpss://doi... .rg. 10 007 978 0300--00--00000 60239-0_3
Yilun Zheng, Sitao Luan, and Lihui Chen. What is missing for graph homophily? disen- tangling graph homophily for graph neural networks. In Amir Glohersons, Lester Mackey, Danielle Belgrave, Angela Fan, UIrich Paquet, Jakub M. Tomezak, and Cheng Zhang (eds.), Advances in Neural Information Processing Systems 38: Annual Conference on Neural In- formation Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10- 15, 2024, 2024. URL baal00/c..c.aaaaaaaaaaaaeeaaaaar
Kaixiong Zhou, Xiao Huang, Qingquan Song, Rui Chen, and Xia Hu. Auto-gnn: Neural architecture search of graph neural networks. Frontiers in big Data, 5:1029307, 2022.
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, and Jian Tang. Neural bellman- ford networks: A general graph neural network framework for link prediction. In Marc' Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (eds.), Advances in Neural Information Processing Systems 34: Annual Conference on Neu- ral Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pp. 29476-29490, 2021. URL https: http::////ccceeei....... www.nniw.....ppppeeeee cc/paper//0211 hast:/6666/36044933cdccc.
cd5cc- Abstract.
Barret Zoph and Quoe Le. Neural architecture search with reinforcement learning. In 5th In- ternational Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017. URL httpps:://opeeeeieee net/ / fort/forum220 htt:://uuumurr/r///m/ 1
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A USAGE OF LARGE LANGUAGE MODELS
We utilize large language models to refine our writing and also employ a large language model- based coding agent assist with code writing. We have reviewed the generated content provided by large language models and will be responsible for the correctness of the polished content.
B SUPPLEMENTARY INFORMATION FOR DATASETS AND TASKS
Our adopted datasets and tasks are summarized in Table It should be noted that we skip the rel- amazon datasets hecause the source is crawled from Amazon, which contains sensitive information and does not present valid license. We then briefly introduce each dataset and task follows:
re hm (Robinson et al. (2024)): This database contains comprehensive customer and product data from online retail platforms, including detailed purchase histories and diverse metadata such as customer demographics and product attributes.
user- For each customer, predict whether they will churn-i.e. have trans- actions-in the next 7 days.
user-item-purchent Predict the list of articles each customer will purchase in the n days.
item-sales: Predict the total sales (sum of prices of associated transactions) for an article the next 7 days.
rel-stack (Robinson et al. (2024) ) This database is about Q&A platform with rep-
utation system. Data dumped from the exas-eex site, and data from the 2023-09-12 dump.
vote- For each post, predict how many votes it will receive in the next months. user-engagemmntt For each user, predict whether they will make any votes, posts, or comments in the next months.
user-badge: For each user, predict whether they will receive new badge in the next months.
user-post comment: Predict list of existing posts that user will comment on in the next two years,
post-post censttreeaeede Predict a list of existing posts that users will link given post to in the next two years.
user-comment count: Predicts how many comments each user will post over the next 30 days.
rel- rel-event (Robinson et al. (2024)): ) An anonymized event recommendation dataset from the Hangtime app, containing user actions, event metadata, demographics, and social relations.
user-repeat: Predict whether a user will attend another event (respond "yes" or "maybe"" in the next 7 days, given they attended event in the last 14 days.
user-ignore: Predict whether a user will ignore more than two event invitations 7 days.
user-attendance, Predict how many events each user will respond "yes" or "maybe" to in the next 7 days.
rel rel-avito (Robinson al. (2024)): A marketplace-style relational database. user-visits: Predict whether each customer will visit more than one ad the next days.
user-clicks: Predict whether each customer will click on more than one ad in the next 4 days.
ad-ctr: Assuming ad will be clicked in the next 4 days, predict its click-through rate (CTR).
user-ad-visite Predict the list of ads a user will visit in the next 4 days.
rel-trial Robinnso al, (2024)): A clinical-trial oriented relational database.
- study-outccmm: Predict whether trials in the next 1 year will achieve their primary outcome.
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study-adverse: Predict the number of patients with severe adverse events/deaths for the trial in the next year.
- sitessucesss: Predict the success rate trial site the next year. condition--spnnsor--no
For each condition, predict which sponsors will run tri-
als.
site-ssonsor-rum For each (site, sponsor) pair, predict whether the sponsor will run a trial at the site.
avs (Wang et al. (0224): DMDave at. (2014)) A Kaggle e-commerce dataset of offers and customer interactions. custone
retention: For each (offer, customer) pair, predict whether the customer will repeat the promoted purchase (become a "repeater") within specified follow-up period. ieee-cis (Chen et al. (2025b); Howard al. (2019)): Transactional fraud-style interactions. fraud: For each transaction, predict whether it is fraudulent at the time authorization.
rel-fl: Fl competition results database.
driver-top3s Predict whether each driver will qualify in the top 3 for a race within the next month.
driver-dnf: Predict whether each driver will not finish (DNF) a race in the next month.
periver-posit tin: Predict the average finishing position of each driver in all races over the next two months.
driver-wins: Predict the number of races each driver will win over the next year. points: Predict whether each constructor team will score any championship points in the next three months.
constructor-scon
driver- cetpposiiiiiioiiiieiioiiiooiioii cionnchange: Predict the average change between driver's starting grid position and final position over the next four months.
rel-arxiive A database recording the publication relation across the arxiv paper-citation: Predict whether a paper will be cited by other papers in the next six months.
author-publication Predict how many papers an author will publish the next six months.
In terms of RDB predictive tasks, Robinson et al. (2024) provides a unified interface to define tasks and generate corresponding training, validation, and test tables through SQL. We then demonstrate an example SQL query for each example task below:
Example entity-level task (user-churn in rel-hm):
1 class UserChurnTask (EntityTask): x"""Predict the churn for customer (no transactions) the next week.
3
4
task_type TaskType. BINARY CLASSIFICATION
5
entity col "customer_ic
entity_ sabile table "customer"
7
time_col col = "timestamp"
8
target_col col "churn"
9
timedelta pd. (daess)
metrics [average precision, accuracy, f1, roc auc]
def 12
(self, db: Database, timestamps: "pd. Series [pd. Timestamp] ") -> Table:
customer db. ta.le dict ["customer"] df transactions
www.taabeeeeee.. ("transactions") df
timestamp of = pd. DataFrame (( ("timestamp": timestamps)
16
df duckdb. sql gann
19
SELECT
20
timestamp, customer id, CAST
21
22
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23
NOT EXISTS SELECT
25
FROM transactions WHERE
26
27
transactions. customer id customer.
customer id AND
t_ dat timestamp AND t dat <= timestamp + INTERVAL 'sself.
29
timedelta)*
30
INTEGER churn
31
FROM
33
timestamp_co df,
34
customer,
35
WHERE
EXISTS (
SELECT
38
FROM transactions
39
WHERE
40
transactions. customer id customer. customer id
AND
41
t_ dat timestamp INTERVAL "[self. timedelta)'
AND
42
dat timestamp
Rn
45
.ddf( ()
46
47
return Table
df=df,
49
fkey fkey_col to hac__coott__pppeeee
col__to_pee__tteeeeeeeeeeeeeeeee (aeyyttabl=sself.eem col: self. eeteentttttllleee col_Noon-, time col=self. time_col
50
51
As see, the time information is split into several time windows and given in the function parameter timestamps, Then, this timestamp will be used to create a time constraint, and the target label will be generated based the SQL logic.
Example recommendation task
in rel-hm):
1 class DaezIteenPurcaaeTTaak Recommendation/aast): """"Predict the list articles each customer will purchase in the next seven days. """
2
3
4
5
task_type TaskType. LINK PREDICTION
sre eec_entttyycom "customer_ id"
7
src_entity_table = "customer"
8
dst dst_entityycol "article id"
9
dst entity table table "article" time col "timestamp"
timedelta = Timedeltt (dayss=7)
12 metrics = htw.__predictiio_precieee link link_predictioon_rccall.
link wtw._pprdiciiionneee
eval_k 12
def make table (self, db: Database, timestamps: "pd. Series [pd. Timestamp] ") -> Table:
16
customer db. table dict ["customer")]
transactions
table_dict
dict ("transactions") df
timestamp df = pd. DataFrame (( ("timestamp": timestamps)
19
20
df duckdb. sql f gunn
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20

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entity_table Published as a conference paper at ICLR 2026
22
SELECT
23
t.imestamp,
iet.ectiio...ustsmmmee.. id,
25
LIST (DISTINCT ionesactionsaartcll. as article
26
FROM
27
timestamp df
LEFT JOIN
29
transactions
30
ON
31
transactions.t dat t. timestamp AND transactions.
32
t. t.timestamp INTERVAL 'sself.
timedelta) days'
33
GROUP BY
34
t. timesstam,,
35
transactions.c customer id
) .df())
38
39
return Table(
40
df=df,
Ekey_col_ to pwpppptklleeeeeeeeeeeeeeeeeeeeee
42
self, sre srr_srree__e_e cete_ennit_coee col: self.sre arr__nnity_tabbe
43
det entity col: self dst detttssttttttttteeeeeeeeeeeeette table,
44
pkey col=None, time_col=self htt.:seelf.tmmmcol...os
47
Similarly, recommendation tasks are based on the joined table between the timestamp table and the target entity tables. A groupby operation is then applied generate the list of target entities.
Example autocomplete task:
def make table(self, db: Database, timestamps: "pd. Series [pd. Timestamp)
-> Table: mable = table dict [self. entity.
2
entity_table table removed cols entity_tablee]] df # noqa: F841
3
dict # noqa: F841
self, entity table
5
removed_coll
6
7
time col tbbtable dtt.dditssleeeeiitttt....... ttmettieeeeeeeeee col entity col db.table dict [self entity table] ppkey_eyper col
8
10
Calculate minimum and maximum timestamps from timestamp df timestamp df pd. DataFrame (( ("timestamp": timestamps) min timestamp timestamp df ("""mmessammp"" .minnn
11
12
max timestamp timestamp_dd ("timestamp") .mx ()
df duckdb. sq] f *"a
16
SELECT
entity table. (time col),
19
entity table. (entity_eee coll,
20
entity_table_remme removee__os (self. target_col)
21
FROM
22
23
LEFT JOIN
entity table remooee cols
25
ON
26
entity_table (entittyco) entity table removed_ cols.
entity col)
27
WHERE
28
(time [cime__co] > (min timestamp)' AND
29
antity_table, Taaee..tin [time_col) <= {max timestamp)'
30
n n
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31
.ddf()
remove rows where self. target_ccttoo is nan df df. dropna (subset= [seff target_coll)
34
35
36
return Table( df=df,
38
fkey fkey_col col_ooooooooooo to poe_yyy_peyy__eeeee (able=( entity_col: self entity_table,
39
40
41
pkey_col=None, time_ sime__co=ttmm_oo_
43
Autocomplete tasks are based on the entity table itself, which is a setting closer to traditional RDB predictive tasks used in Wang al. (2024a).
Table Summary of databases, tasks, task types, and evaluation metries used in our experiments.
Database Name
Task Name
Task Type
Metric
driver-dnf
classification
ROC-AUC
driver-top3
classification
ROC-AUC
driver-position
regression
MAE
rel-fl
driver-wins
regression
MAE
constructor-scores, points
classification
ROC-AUC
regression
MAE
user-churn
classification
ROC-AUC
ser-itemm-urchass
recommendation
MAP
rel-hm
item-sales
regression
MAE
post-votes
regression
MAE
user-engagement
classification
ROC-AUC
user-badge
classification
ROC-AUC
rel-stack
user-post nee-post-comment
recommendation
MAP
post-post-related
recommendation
MAP
user-comment-count
regression
MAE
user-repeat
classification classification
ROC-AUC
rel-event
user-ignore
ROC-AUC
user-attendance
regression
MAE
user-visits
classification
ROC-AUC
user-clicks
classification
ROC-AUC
rel-avito
ad-ctr
regression
MAE
user-ad-visit
recommendation
MAP
study-outcome
classification
ROC-AUC
study-adverse
regression
MAE
rel-trial
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Published as a conference paper at ICLR 2026
C SUPPLEMENTARY THEORETICAL DISCUSSION
C.I DEFINITION OF METRICS
In this section, we present the formal definition of missing homophily-related features adopted in this paper, diseussed in Section 4.1.1
Definition 2 (Class-insensitee
homophily). For classification task with class prior प Euer. 8u E AC define the class-condition edge similarity for metapath m by
Σ
(Snu:S)) Bu.k
hu (G:m) = (u.o)EEm
(k= -b=11 1k......,C) C)-
Dn.k
(a,t)EEE
The insensineeeeevve homophily for m is
Hins (G;m)
1
[laa(C:mm) Tk
C-1 k=1
For regression tasks, where class imbalance is irrelevant, we set
Him(GG; m) = Hedge (G;
Definition 3 (Aggregation homophily). Let (m( = {v E I. (u Em be the labeled m-neighbors of u and degm. (u) = [ (u)]. Define the neighbor-aggregated label
y(()) = degm (u)
Σ
Yv
(classification),
glm)
Σ vETm(M)
de (regression).
deg (u)
mETm(()
Let Um {u E : deg (u) > 0). The aggregation homophily for m is
K Kyu,, y!m)) classification,
H Hagg (G:m)
U
Buu V4 )/ regression,
uEU
with K dot product for classification; correlation-basee metric K K(a,b) = äb (a-w)(b-11) for regression, where p and o² are the empirical mean and variance of labels over labeled nodes.
o²
C.2 WIYY RDL BETTER AT LOW-HOMMPPIıLY REGION FOR THE CLASSIFICATION TASK?
In this section, we analyze why RDL exhibits advantages in low-homophily regimes. Our argument adapts the Bayes-optimal analysis of Wei et al. (202)), which characterizes how the optimal one-hop classifier changes with label/fcature homophily. We do not provide original proof here; instead, we specialize their framework to metapath metjepet-prroeeced RDB graphs and use it to explain the observed behavior of RDL.
Following $4.1.1 let G (V,E) E) be the heterogeneous graph induced by the RDB task with labeled entity type and node set Vy. Let M be a finite family of self- metapaths m that start and end work with binary label space Y, E {-1,+1} drawn i..d, with prior Pr(Y, = +1) = π E (0, 1). We next specify the generative model that underpins
at F; each induces edges &m VF and neighbor sets (m) For clarity,
analysis. We make the RDB data fall under the framework of Wei et al. 2022) by considering the metapath-induced graphs.
Definition 4 (Metapath-ww -iise
Let VF be the labeled entity set and M a finite set of self-looped metapaths on F. The generative model for (Y, X, {Em ImEm) is:
wise contextual SBM (ICSBM)).
(Labels). Each node v E VF has a class label Y, E {+1, -1] drawn i..dd from a prior Pr(Y,- 1) 10
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(Node features). Conditional on Yu, the attribute X. is drawn i.i.d. from class- conditioni distri- bution Py, with density p(- Y2) (e.g., a Gaussian mixture). Features are conditionally independent across nodes given Y.
(Edges along each metapath). For every metapath m E M, conditional on labels the edges in Em independent and
Pr((u, v) Em Yo) Pm
Pr((u, E Yu ym-
Moreover, edges are conditionally independent of features given labels: {&m Fm 11 IY.
Remark (Metapath-indueee induced graphs as the substrate for DES and RDL). Def. F specifies, for each self-looped metapath m, an induced edge set &m on VF. In practice, both DFS and RDL operate F-F graph: one first projects the heterogeneous joins along m back to (e.g., via path counts or normalized weights) to obtain an adjacency A(m), and then aggregates informa- tion over A Concretely, DFS produces non-parametris linear aggregates (e.g., SUM/MEAN) of base features on VF through A(m) (or its row-normalized form), which algebraically coincides with multiplying by Alm). In contrast, RDL the same melapath-inducel structure but applies velation-aware (per-metapath) transformations or gates to the propagated signals before or during aggregation. Thus, after the metapath projection, both methods are defined on the same F-F graph if DFS only utilizes the mean aggregator; they differ only in whether the propagation is purely linear and fixed (DFS) or relation- Itution-conditioonnn and learned (RDL).
this metapath-
Then, following Wei al. (2022), consider the MAP estimation of the classifier that can mini- mize the misclassification rate. The estimation a node label depends on its own attributes and the attributes of its 1-hop metapath-induced neighbors.
The one-hop maximum a posteriori (MAP) rule at node v selects the label y E (-1,+1) that maximizes the joint posterior of and the (latent) neighbor labels (yu (yw)weu, vgn) given the local observations (Xe, {Xw]: {1{(0, E Em))
= arg max max Ty (Xx, vE(1I} (ow)
II
II
Tun p(X.l Bu) P (ym=9) (vntt))
Om
MEM
uEN!!)
Taking logs (monotone) and subtracting the two class scores yields a decision function whose sign gives Y::
max X( +1), 9m pl X1 -1)3 max Hm pl X +1), Pm pl X
Y, = sign( log I- = + log -
log
mEMM
KEN(M)
v(x))
Prmax (Xx); 7m))
where Ym := log Pm encodes metapath homophily and (X.) := log p(X.++)) gm
p(X is the feature log-
likelihood ratio. The per-neighbor MAP message admits the closed form
@max (s: log max(e?+s, 1} clip(s, -7, +7),
max(e*, e*}
Basic properties of the MAP message metapaths. For m M let tm log Pm and define gm
the per-neighbor MAP message dmax (x: 7) = clip(s, 77,7) applied to 8 (X.)).
Lemma (ffte-off, flip. and linear region). For all s e RR and m M.
(i) (gate-off) if 7m = 0 then @max(s: m 0:
(ii) (flip) 7m < then Φmax (s; 7mm) Pmax (s: lyml):
(iii) (linear region) [s| S Tm then dmax Ymm)
Proof. All three statements follow immediately from the piecewise form of Φmax (s: @ ( = -y for s A -7, equals s for [s] < 7, and equals +r for s 2 7. In particular, 7 gives the map; replacing 7 by -y flips signs; and on [-7, 7] the function is the identity. n
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Remark 2 (Interpretation of Lemma D. Lemma 1 summarizes the three key regimes of the MAP message mmax(s;; (s: These properties clarify how structure and features interact: (1) when y = the metapath carries no information and should be shut off (gate-off); (ii) when y < 0. the neighbor evidence must be flipped to align with the center label (heterophily flip); and (iii) when |s| S 7 the nonlinearity reduces to the identity, So in highly homophilous settings, the MAP rule coincides with linear DFS-style aggregation. These simple facts underpin the later SNR comparison: they explain why linear propagation is adequate in strong homophily, but why Ataation-warr gating is necessary and beneficial in low or negative homophily regimes.
We then come up with the vectorized form of the MAP score vector by aggregating each element Proposition 1 (Vector form on the malized) metapath-induced adjacency on VF obtained by projecting m back to F; put E RIVE with 8o (X,). Writing Umax elementwise, the one-hop joint-MAP score vector is
F-F graph.), Let ((m) be the (possibly nor-
== log xx 11+++ +
Σ MEM
A Qmaxx 8: Tm)) Y. sign(Zz))
(1)
Proof. For each v E VF, the one-hop joint-MAP score derived earlier is score(v) = log - + (X,) Em Eue @maxx(/ (X..): 7m). Let R/ with s, (X,) and define pm (s) elementwise by [9m( = Pmux (Sui 7m). By the definition of the metapath -poppttt- adjacency (mm) [dm (+)]a. = EmeNgmn Pmax (8u; 7m), which reproduces the inner sum for metapath m; summing over m gives
(mm) (with entries ((m) supported on E N(")),, we have (A(m) om (s)]r- En A/2
the full neighbor contribution. Hence the v-th coordinate of z := log - + s + Em Amm) om equals score(u), and the MAP decision is Y, sign(ze), proving the vector form.
We then introduce key concept of signal-to-noiss ratio (SNR) to compare the linear DFS-style aggregation and the gated RDL-style aggregation. The SNR a standard metric in statistical sig- nal processing and communication theory that quantifies the strength of the signal relative to the noise. In our context, it measures how well the aggregated neighbor information can distinguish be- tween different classes, taking into account the variability introduced by the features and the graph structure.
Definition 5 (SNR bookkeeping on metapaths). Let j4 == (X)( Y = +11, p_ == (X)
=--1), 6 == μ4+ uu-, and let o² max (ar(((()) Y +1), Var( Y -1) /4
(to upper bound the class-mixture variance). Denote the expected degree dm == B((.N(( and
amm = Pr(Y.-Y, {u, r) E Em) -Y tu E Emm) Pm qm tanh
Tim
2 ) (2)
Pm + qm
so that Pr(Y. {u, o) E Em) = (1 + am-ma.) )/2.
We then introduce an assumption that controls the gap between the variance of the sum of metapath- wise neighbor contributions and the sum of their variances. It should be noted that the proof here assumes the feature is in the informative regime. covariance control). There exists A 2 1 such that for any choice of (centered) neighbor functions Z Zm) = Emendm, (m)) En Zm(lv) Y.) S under conditional independence across metapaths.
Assumption
Em Var Var(Zm (v) Y.). This holds with
Here Im : X - R. denotes the mer-metapath neighbor contribution for m m....g. in the linear/DFS case Im (I) = (xx) [[((X) Yu), while in the gated/RDL case 9m gm.(z) = Omaxx (1b())))))))))
Eldmaxx ( (x)) 7m) Y=] (both centered given Y.).
Lemma 2 (aran-variance ledgers for linear vs. gated aggregation). Consider the linear (DFS-style) neighbor sum Sti(()) = Em, Eve ((x.) and the gated sum Sgaze())
Em EMENST) @max (b (b(X)): 7m). d, denotes the degree of metapaths. Then
BjSpin Y = +1] - E|Sm Y, = =--- Σ
dm Om 6,
Var(Sin YYo) S A Σ dm o².
m
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Moreover, in the informative-feature regime Wei al. (2022 Lemma. CI).
S|State Y = +1]-BB[gaaee Y. - 11]= ~ Σ m
2dm O'm Tim
Var(Sgate Yo) S Σ dm 0 m
m
with o S Im e- for some universal c > O (here A denotes class-separation measure, e,g,, the Gaussian separation or a calibrated logit separation).
Proof. Linear mean. By exchangeability along metapath m and linearity expectation,
B
Σ
(Xx))
Y, =g] = =d (X,) y fu, v) E E.
WEND
Conditioning on Y, and using Pr(Yu = y/ edge) L+
B(o(X.)) y.=y, edgel 1+0m My
1- p-y-
Subtracting the two classes yields dn am (p= 1_) dm a. Summing gives the first display. Linear variance. For each m, Var(> < uEE 10 (Xx) Y,) & dm o² by Ainomial-variane bound
and the definition of 2². Assumption gives the sum
m..
Lemma. C1) together with their Gated mean. Using the same conditioning, and Wei et al. 2022
regime analysis, have [m[maas (asimaaxx//((e (m) Yu +1 Therefore
+ym in the informative regime.
1+ Eldmax (epmmax(bb(ii)r (Ku): Yu y, edge) RV
2 Ym + 1-am (-7m) ) = Ory 7/m>
2
So the class difference is zs 2d amm Gated variance. By Wei et al. 2022 Thm. 2), the class-
conditional variance of @max (s); 7mm is at most Ch -ea" for constants C, > 0. Summing over Thandles the sum over m.
neighbors contributes factor dms and Assumption [
Definition (Metapath-lewe) level SNR proxies). Define
(mm dm Oen 5)² Plin A dm o²
(Em dm Ore 7m
Pgate
A Em o
dm
22
By Wei et al. [2022] Thm. 2) (single-relation) larger SNR implies strictly smaller misclassifica- tion error up to universal constants; we use Plins Prote as proxies for multi-metapath graphs under
Assumption 7
We then discuss the high-homophily case.
Proposition 2 (Multi-metapath DFS equivalence under strong homophily). If for every active m one has Pr([v(X)) 7m) 2 1 Em, then
log --
Σ (m) Omax (85 log xT 1 1 A (m) + r.
Σ
I-x
where ||r|] 5 Em Em (m))
1 1l2. In particular, Pgate = Pii l o(1)) as max Sm - 0.
Proof. By Lemma Iiii,, (ma ) = & whenever |so| S Ym- Write the enror vector c(m) = @maxe (8; 7m) 8, which has support contained in Em == {o |pp > 7m and satisfies [|e((m) S
max [18o] You Ymm) S 2/ Then
Σ (m)) @max (m)) Σ (m)) + Σ A (m) (m)
By Markov and the assumption, Pr(c E Fm) S Em, so 1 (m) { || S (m)) 1 [|e(m)) | Pr(Em) < E ( 1m]] for a universal C' (after rescaling s), giving the stated bound with r = Σn ((m))(m)) The SNR statement follows because replacing @max by s changes the mean and variance only the rare set F.,-
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When nonlinearity is necessary in the multi-metapath setting. The next theorem upgrades Wei ct al. (2022 Thm. 2) from a single relation to multiple metapaths by summing contributions under
Assumption
Theorem (Multi-metapathh gating advantage low/negative homophily). Assume the feature sep- aration is in the informative regime of Wei er al. 2022 Thm. 2), so that each active metapath m
admits o < T e efethher (i) there exists least m_ MM with 7m_ 0 (heterophilous metapath), or (ii) non-negligible subset My satisfies l'mll S e (near-zero homophily). ) then there exist constants C,d' d' >> =0 such that
Pgate 2 Ceda? (Em dm Tmll2 Em tin Im
8² (Em dia arm)
while
Plin
Em dm
Consequently, whenever the signed sum Em dm Om is small due to sign-mixing near-zero ho- mophily, one has Pgate > Pins and the gated aggregation strictly dominates linear aggregation.
Proof. By Lemma Thm. 2),
Pgate
( dm Timm? A ALi. dm
Using o S ci from Wei al. 2022
Vm )2 ACie-ca oA* 2m dm n
(Em dm Am
2 Cecss ( dm 7ml)²
Pgaae
2
Em dm??
absorbing constants into C.C. For the linear SNR, Lemma gives Pln AEm dmot
D dm
En dmm) -m dm (after rescaling A into the constant). Under condition (i) or (ii), Em Umam can be made small (sign-mixing and near-zero homophily, respectively), whereas Em dm Amlmmmml remains of the order En dm 7 because am = tanh( has the same sign as and la 2 hmml for small 7mml Combined with the exponential factor e this yields Pgate >> Plin-
Corollary (Sign-mixing amplifies the gain of gating). If there exist my and m_ with Ym > 0 and 7mm then
Pgate
2 e d'
(m dm amm Tma
Em dm 7
52 =
Plin
(mm dm laml)
Em dm
So the advantage grows with the degree-weighted sign diversity across metapaths.
Corollary 2 Zero-information robustness). If 1mml 0 for a subset Mo (no homophily), then these melapaths contribute nothing Pyate (by Lemma ii) but still inflate the denominator of Pla decreasing the linear SNR. Thus, gating is robust to uninformative relations, whereas linear averaging is not.
Em de and Tabs = Em dm
Corollary (Average-homophiiy can misleading). Let Tavg
dieclimall Even if avg 0 (net assortative), when the disagreement Fabs [Favg] is large (sign- mixing/heterogenetty across metapaths) and features are informative, one still has Pgate >> Plini hence average homophily alone does not decide in favor of linear aggregation.
Lam dhe
C.3 SAMPLF-SIZE DEPENDENCE OF RDL VS. DRS
We complement the homophily analysis by explaining why the number of training rows Nuain sys- tematically modulates the RDL-DFS gap. The key observation that RDL realizes strictly richer hypothesis class than DFS (due to learnable, relation-specific nonlinear aggregation), so it enjoys smaller approximation error but larger estimation error. Standard generalization bounds then imply sample-size threshold: DFS tends dominate in low-data regimes, while RDL becomes superior once Niain is large enough. This argument applics to both classification (cross-entropy loss) and regression (squared loss), and do not distinguish them below.
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Setup and error decomposition. Let 2 denote the space of task-table rows (after feature/aggre- gation), and let -):-)) be a bounded. L-Lipschitz loss (e.g. cross-entropy or squared error). For a the induced prediction class, and by pipeline {DFS, RDL), denote by Fx {f Z - R
R(f) E [((f(Z))))
its population risk under the task's data-generating distribution. Let (x( be the empirical risk on N training rows, and let fx arg minger, Rv(J) be the empirical risk minimizer (or approximate one).
Lemma (Approximation estimatii decomposition), For each pipeline n, define the Bayes risk R == i R( the approximation error A₂ := inf (R(f) -R and the estimation error E, (, = == E [[[(f=] jnf R(f)) Then
E [[[[(f)] - A, E (N).
Moreover, (F*))(=))))))))) denotes the empirical Rademacher complexity of Fa on N samples, then there exists universal constant C >> 0 such that
(N(NN & (Fa)
The lemma is standard from statistical learning theory:
Ar is purely bias (approximation) term, while E (N) is variance (estimation) term controlled by the complexity of the function class.
Lemma 4 (Capacity ordering of DFS and RDL). Let dpes be the dimension of ΦDrs (z). and let dRDL- = ddrs + dgate be an effective representation dimension at the input of the last linear layer of To. where dggae > accounts for the extra channels created by the encoder-GNN stack.
Then:
(Expressivity) FDFS Ç. FRDL- In particular, the approximation errors satisfy ARDL S ADFS, and are strictly ordered on nontrivial tasks.
2. (Complexity) There exist constants CDDS; such that for all sample sizes N,
(NDFF
CDPS
Rx((FRD)
CRDLI
VN
with Cx x da and thus CRDL > CDFS whenever dgate
Proof. Expressivity. By assumption, eœ and Gg can be set to implement the same deterministic DFS- style aggregates as ÓDFS (or to just pass those features through), and ræ can simulate any E Gtab up to approximation error. Hence every g Φprs is realized (or approximated arbitrarily well) by some fo. giving FDrS S FRDL. The extra relation-aware nonlinearity in Go yields hypotheses that cannot be written as g ΦDrs, so the inclusion is strict generic tasks.
Complexity. DFS has no learnable parameters ΦDES; only the tabular model contributes to es- timation error. RDL, in contrast, learns the encoder, GNN and MLP. Under standard norm con- straints on these modules, classical results give (F=))) S vda/VN for v E (DFS, RDL]; the encoder- GNN stack strictly increases the effective dimension and parameter count, so CRDL >
CDFS.
Theorem 2. Let fors and FRDL. be empirical risk minimizers in FDFS and FRDL. trained on N i.i.d. labeled examples (classification regression). Decompose the expected risk as E[R(fa)]
An + E,(N), where Ax is the approximation error and E, (N) the estimation error of pipeline ा.
Assume that RDL has strictly smaller approximation error:
ADES -ARDL = A > 0,
and let CDFS, CRDL be as Lemma f with CRDL CDFS. Then there exists a universal constant C > 0 such that for all N,
C(CRDL - CDFS)
E [((fDE))] [[[[(RDD)) > A
VN
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Define the crossover scale
C (CRDL. (Drs)
No:=
A
Then:
N < No = B(R(fors)
< E [[((RRD))],
N No B[R(FRDL)] [R(fDrs)]
Proof. By definition,
E [[[[fors]] - B [[R[fRDL)] (ADES ARDL) + (EDrs(N) - ERDL(N),
Using Aa 0 and the Rademacher bounds Ex(N) (Fx & C Cv/VV,, obtain
EDrs(N) ERD 2 C (CRDL CDPS)
VN
Combining with the approximation term yields the stated lower bound. The threshold No is exactly the sample size at which this lower bound becomes nonnegative, giving the crossover conditions.
Interpretation for Ntrain Identifying N with the number of training rows Nurin Theorem 2 for- malizes the empirical trend that the DFS-RDL gap controlled by a bias-variance trade-off: When Nuii is small, the variance penalty x (CRDI. CDFS) / mami dominates and the simpler DFS pipeline safer. Once Numin >> No. the complexity term vanishes and the approximation advantage Aa of RDL dominates, yielding systematic performance gain. The argument only uses generic generalizations and bounds and therefore applies uniformly to both classification and regression losses.
C.4 RELATIONSHIP BETWEEN RANDOMLY INITIALIZED MODEL AND HASHING
Following the notation Section 3.3 consider the (atemporal) attributed, typed graph
G 0VV EV, vi, Jv. fE);
where Φ V NodeTypes maps entities node types, vb E LinkTypss maps links relation types, and fv V + Rav fE: - -R-Rda Rdæ provide node and link attributes.
NBFNet message passing. NBFNet can be viewed as dynamic programming (DP) over the schema graph, replacing Bellman Ford's sum and product by learable operators. Fix state width E and horizon T E N. Each node v E V maintains a d-dimensional state H(0(v) E Rd at DP layer = .T.
(Indicator)
(v) = lo(1 8) E R
(3)
(Message)
(H E(u-v)) ((( @o (E(u-vo)) E Rd
(4)
(Aggregate) (() + (H((-(()) {(u~v))) f= T.
(5)
(n-tOEE
Here {uu+v)) E X' bundles the schema edge context (e.g.. r(u and endpoint types via Φ). All functions are chosen permutation invariant over incoming edges. There three key design choices of NBFNet.
Indicator. A simple indicator is ls s) = 1fv 3e demt(t)) where demb (v) is learned (or fixed) embedding of the source node type E V to distinguish sources from non-sources.
Readout. Given source node E
Vera. we pool across layers and endpoints to obtain
T
Zo(s) = Σ aF
Σ DEV
( (H e R
(6)
E
where 11(0 Rd -4 is an optional projection (identity if not needed), (ar) are length weights, and B V R2z selects/emphasizes endpoint types.
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Edge representation. For graphs induced from RDBs, edges come from PK-FK relations. We abstract each edge as a discrete edge token T(e) E Σ with
(e) (e),, (e)), ((head(e))) E E,
so that any path (e1,.... ................................ maps to the token sequence t(p)
e E
The Bellman-Ford-eet recursion multiplies edge-local "messages" along a path and paths and endpoints. Unrolling the recursion, therefore, yields a path-wise expansion in which each coordinate collects contributions from every typed path reachable from the source.
all
Frozen NBFNet as random features. We first replace the learned message operators by random scalar maps and show that the resulting DP computes random features that aggregate typed paths.
Fix width and horizon T. For each coordinate k [d] and layer & E [T], independently sample
9 X + R.
Elgh
(2) (x))
Flgh
10 (z) 9 (z)] x(x,z'),
for a positive semidefinite (PSD) kernel X x X R. Intuitively, gk (E) is the L-th message coordinate of an untrained NBFNet at random initialization.
Proposition 3 (Path-wise wise expansion of frozen NBFNet features). For E Vir and E [d], the k-th feature computed by frozen NBFNet admits the path-wise form
(s)) Σ aL Σ
(II
(II
L
9e (E(ce)) @(hcad(p)).
L=1
pEPr(
t=1
2(8) = (z) za(s)) E RRd
Proof. Start from the layer recursion; each step distributes over incoming edges and multiplies by the layer-l message 9h (.). Inductively expanding to depth enumerates all length- paths p= (ei...., eL) from producing the product of edge messages along Pooling with weights az and endpoint weights 3 yields equation
Droommi-nn realization. The expansion in equation 7 can be evaluated in ( EE) per feature by the following Food-sn-Ford-stylee recurrences:
14 (0) (v) 1(v = s}
12 (f) = Σ
g (E(U (E) 1/6 (w),
(u-w)E
(8)
(x(s)
Σ ae Σ
3(v)
h(v) (0) (v).
This view makes clear that frozen NBFNet is a DP that sums over paths while multiplying edge- wise random features.
Step 2: The induced kernel. We now identify the kernel implicitly computed by these random features.
Define the finite-width kernel
Ka(s, = _ 2(s') (s, E Verc).
Theorem (Anchored typed-path kernel). With the construction above,
( (elee)).(E))) ((head(p)) (head(g)),
B[Ka(s,8)] = Σ L=1
Σ
(7)
( DEPL (s' (mo)
where p .1.) and ( (Ji,...,Iu) ST.) The right-hand side defines PSD kernel K on Vsr
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Theorem 3 follows by expanding zss) z(s'). observing that mixed lengths cancel due zero mean, and using layer-wise independence to factor expectations across edges. Thus, a randomly initialized NBFNet computes random features for the typed-pal kernel K.
Proof. Expanding 2x (*{s((((s)) and taking expectations over {gg ( mixed-length terms vanish by zero mean; for equal lengths, independence across layers yields the product of second moments
I x(E(ee). ((fe). Summing over paths and averaging over is gives equation since K is expectation of Gram matrices.
PSD follows
Step 3: Concentration at finite width. Having identified the limiting kernel, we quantify how fast Ka concentrates around K.
Lemma 5 (Concentration). Assume each 9k (.9() (x) is subgaussian uniformly in with proxy a², and sel v sup; k(2; a)) < oc. Let N1, +) = [Pr *] If E az ve (1 < oo for every then there exist constants C > 0 such that for all 0.
Pr([Ka(s,6') EKa(s, 801 2 8) 22xp( cdd /03)
Hence Ka -> K in probability at rate O=(1/Vo).
Lemma 5 ensures that training only linear classifier atop z(-) realizes a standard random-feature approximation to the RKHS induced by now specialize the kernel choice to make the hashing connection explicit.
Remark 3. Training only linear classifier on z(-) implements random-feature learning for the kernel K; as d + co, the solution converges to the kernel method in H.K.
Step Discrete edge tokens and the Dirac kernel. Let T(e) E be a discrete edge token (e.g., (e) or (é(tail(e)) (ee), of (e))) and consider the Dirac (identity) kernel
ks(a, x') 1 (T(x)= = TTk')
Realize Kg with Rademacher codes by drawing, each layer random maps (()) : + {+1]a
independently across l and setting 9% (x) r (1) (1) (r(z) Then Theorem yields
Ka, 8')
Li Σ = ((i),)= T(fi) #(head(p)) ((head(g)). PEPL(e-H)
L=1 (EP2(s'mM)
(8)
tee--f-typee-pathhe view. Let V, EST be the bag of typed path sequences of s, where the
coordinate for o (oI...., (.....,,,)) σL) 1 is
[o] ag x (count of length-L paths with T(p) = a. weighted by ((head(p)))
Then equation g is exactly the inner product E[Ka(s, s')] = (W., W.r). Moreover, each random
coordinate implements multiplicative sign code over sequences:
(*)s)=
Σ GEEST
V.
9 (a.).
T2(O)
"Normalization: we deliberately omit a 1/Vd factor inside 9h outer 1/d Ka provides the correct scaling. Inserting 1 1/vd inside each layer would undesirably shrink longer paths by d
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Sparse CountSketch/Tensorrse realization. We then bridge the bag-of-typed view to the countsketch algorithm. First, introduce pairwise independent hash functions
6)] - [d],
( : - [+1), i= 1=...... I....,T.
For typed sequence (a) ( σI.) define combined bucket and sign
(()) (01) + +h40 (o1)) mod d,
S(o) II 8 (o)),
and the sketch y(s) IR by
U,(s) Σ
V. 1HH()) =s). j E [d]
(10)
BEEST
This is the standard TensorSketch construction specialized to sequences (metapaths). Then: Proposition 4 (Unbiased CountSketch of typed-path bags). With the construction in equation 70 using independent (09)) and g(t) with pairwise independence, we have
[(((s)))((')) (V.,, Vz),
and
1 d
(ar((u()), 25
= H!o') j} and Proof. Expand ((s)),(s')) E.... V. Wo
take expectations. The sign hashes kill cross terms (a + a') by zero mean, while bucket colli- sions contribute only when H(r) H(o'): pairwise independence ensures these events occur with probability 1/d and cancel with the outer sum over j. Variance follows from standard CountS- Sechor analyses using limited independence.
How frozen NBFNet implements the sketch. The dense realization (A) is exactly what equa- tion 8 computes when (2) (z) = (2) (r(z)) each DP layer multiplies by layer-& signs, and aggre- gation sums across paths precsel the linear form in equation 9
Takeaway. Steps 1-3 show that randomly initialized NBFNet realizes random features for typed- path kernel; Step reveals that, under a Dirac edge kernel, those features are precisely CountSketch- style projections of the bag of typed metapaths reachable from In short: frozen NBFNet DP- powered CountSketch of typed-path counts. This perspective clarifies both the inductive bias (which typed patterns are matched) and the approximation behavior (controlled by d, T, and the path growth rates).
D MORE RELATED WORKS
D.1 RELATIONAL DEEP LEARNING MODELS
To effectively address the challenges in RDB benchmarks, Robinson al. (2024); Wang et al. (024a) propose GNN-based pipelines with two main components: (1) transforming original tab- ular features into unified latent space using type-specific encoders, and (2) aggregating latent features with a temporal-aware GNN conditioned on primary key-foreign key relationships. Chen al. (2025a) further extended the message passing function to capture higher-order information by introducing atomic routes. Yuan et al.] (2024) adapts the original GNN for recommendation tasks by implementing a path-based routing mechanism combining an ID-based GNN (Zhu et al. 2021) with shallow learnable embedding- based retrieval. [wwieeii et al. (2025) explores the potential of transformer-based backbones for RDL tasks; however, these currently require significantly more computational resources than GNN-based methods for limited performance gain, we do not in- clude them in our design space.
Wu et al. (2025). y Wydmuch et al. (2024) investigate the potential of large language models for pre- dictive tasks on RDBs. Currently, they exhibit a much lower performance- -etraaccc-----eouceee ratio than GNN-based methods, and is difficult to evaluate the influence of duplication between pre-training
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knowledge and downstream tasks. We thus leave their study future work. At broader scope, recent graph foundation model and graph-LLM works (Mao et al. 2024; Chen et al., 2023; Wang
ct al. 2025a) offer useful perspectives transferability and pre-tramingg but their implications for
Temporally grounded SQI-defined RDB prediction remain under-explored.
Compared these models trained from scratch, Fey et al. (2025); [Wann al. (2025b) propose
foundation models for RDB tasks. Wang et al. (2025)) relies on a cross-table attention module that mimics DFS aggregation, but its performance cannot consistently outperform state-of-the-ar GNN- based methods. Fey et al. (2025) utilizes graph transformer-basee performance and in-context learning capabilities. However, it is not yet open-sourced, and training details are not revealed. With the help of tabular foundation models like TabPFN (Hollmann et al., 2023). is also possible to achieve in-context learning by utilizing online DFS to achieve promising performance.
backbone and delivers superior
D.2 AUTOML FOR GRAPH MACHINE LEARNING
AutoML (Hutter al. 2019) seeks automate expert tasks data engineering, model engineer- ing, and evaluation into an end-to-end machine learning pipeline. Model engineering typically encompasses neural architecture search (NAS) (Zoph & Le (017) and hyper-parameter optimiza- tion (HPO) (Bischl et al. 2023). Many works have adapted AutoMI. ideas to GML: for example, Gao et al. (2019) and Zhou al. (2022) use reinforcement learning search architectures, while Yoon et al. 2220 applies Bayesian optimization to improve search efficiency together with an algo- rithm budget constraint. One-shot NAS approaches first train a supernet and then prune it to obtain target architectures (Li & King 2020 Guan et al. [2022] Qin et al., [02II, and Zhang et al. (2023) extends this paradigm to dynamic heterogeneous graphs. However, supernet- based methods are not well-suited to RDB settings due to the heterogeneity of model designs required there.
Beyond model-centric search, data-centric AutoML. leverages dataset properties to guide selection. MetaGL. (Park et al. [223a]b) uses structural embeddings and graph statistics as task embeddings for meta-learned GNN selection. GraphGym and AutoTransfer (You et al., 2020 Cao et al. 2023) fol- low a knowledge-transfer strategy; AutoTTransfe in particular constructs loss-landscape-bisd
task embeddings and employs pre-trained embeddings to steer HPO. In contrast, our work is driven by empirical observations and targets architecture selection at both macro and micro levels, especially across heterogeneous model classes (RDL and DFS) in relational-database predictive tasks.
Bai et 2021): Luo al. [2021) Lam et (201) propose dedicated systems for AutoML over relational data. These efforts share DFS's motivation for automatic feature engineering but generally lack ready-to-use open-source implementations; accordingly, adopt DFS representative,
practical framework for automatic feature engineering.
Ranjan al. 2024) studies post-hoc model selection and argues that picking what deploy purely by the "best base validation score" is brittle.
D.3 REAL-WORLD GRAPH MACHINE LEARNING BENCHMARKS
Benchmarking is essential for evaluating methods in graph machine learning. Representative datasets include the Open Graph Benchmark (Hu et al., 2020a) and TUDataset (Morris al.) 2020). However, recent work questions whether these benchmarks, like predicting the category of academic papers, reflect real-world tasks (Bechher-Speicher et al., 2025). To address this gap, benchmarking GNNs on relational-databas. (RDB) predictive tasks has become increasingly popular; notable ex- amples are 4DBInfer (Wang et al. (222a)). H2GB (Lin et al. 2024), and RelBench (Robinson et al.) 2024h. In particular, RelBench provides SQL-based framework standardize task generation, which has helped become a widely used benchmark for RDB predictive tasks.
In addition to model-centric benchmarks, recent works propose benchmarks focused on graph struction (Chen et al. 2025b] Choi et al.) 2025). Jointly studying automatic graph construction and automatic model selection is promising direction for future research.
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E SUPPLEMENTARY INFORMATION FOR MODELS
In this section, we present more details model designs, including model parameter design space and more training details.
E.I DISCUSSION ON THE IMPLEMENTATION DIFFERENCE BETWEEN 4DBINFER AND RELBENCII
It is noteworthy to discuss the difference between the implementation details of 4dbinfer (Wang) et al., 2024a) and relbench (Robinson et al., 2024). which are two main frameworks used to re- search RDB prediction tasks. We find that these implementation discrepancies are essential for fair comparison between macro-level and micro-level architecture comparisons.
Feature encoding part. First, Relbench and 4DBinfer both adopt type-specific encoder to project categorica/nmmmrricl/lexx into a latent space with the same dimension. However, after this transfor- mation, Relbench further adopts tabular encoder to transform the latent embeddings, which is not present in 4DBInfer. Furthermore, due to the implementation differences between Pyg and DGL, 4DBInfer does not utilize relative positional encoding when encoding temporal features. Another difference is that 4DBInfer will normalize all features. To align this with Relbench metrics, we save the scaler and do the inverse transform at the test stage.
Neighborhood sampling part. Both frameworks adopt a temporal sampling strategy to avoid tem- poral leakage. However, there a difference in implementation details. For relbench, adopts the temporal sampling of PyG, which generates disjoint subgraphs (when using the latest neighborhood sampling). This aligns with the time dynamics of the relbench task, such as user behavior over the past few months. For 4dbinfer, it is similar to standard subgraph- sampling with a time mask.
Evaluation setting. There is also a difference in how the two frameworks do the evaluation, es- pecially for the recommendation task. 4dbinfer adopts pre-selected negative sample set, whereas relbench uses the entire target set as candidates. Moreover, 4dbinfer focuses on bipartite graphs, while for relbench, there are some tasks where the source and target lie between several-hop metap- aths. This makes the method design not compatible across two types of problems. For example, any methods requiring pairwise information not scalable for relbench settings.
E.2 DETAILED TASK FEATURE DESIGNS
In this subsection, introduce the detailed task feature designs.
Simple heuristic performance It characterizes the target entity distribution across differ- ent data splits. Specifically, we compute entity mean_val, wesiitymmeiin...mm entity.mean.trr betty_emn.ttrinn and entity www.mmiinnrrinnnnn which capture the central tendency of tar- get entities in both validation and training sets.
For example, for entity mean, first generate the following prediction,
I fkey = 1ist (train table.
col to pwtpppeeetllee keys [0]
2 df train tawltttallee.ee ..lee..f. df. groupby (fkey) agg ((task. (atast col: "mean"))
3 df rename (columss target col: target "), inplace=True)
4 df pred_table table. df. merge (df, how="left"" on=fkey)
5 pred df[" ["_ target_ fillfin (0) astype (float). values
Then take the performance the validation
feature.
Homophily stats temporal It agagregates 13 model-free heuristics designed to capture intrinsic properties of the relational structure and task characteristics without requiring any model training. This group synthesizes four distinct categories of structural features:
(1) Homophily Features: We compute five adjacency- based correlation statistics
h.adfjs.corr.mean. h.ad j.adjs.corr.max h_adjs.corr h.adjjscorr.mode. and adjs corr weighted mean. The weighted mean variant accounts for edge importance based degrees.
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(2) Temporal Autocorrelation Features: We include two lag-based autocorrelation measures lagl_ aogg_aattocorr.oo... and oag2. wwwoorrrcorrrrooooooooooo that capture temporal dependencies in tumeawaree relational tasks. These features measure whether target values at time t correlate with values at / - and t - 2, respectively.
(3) DFS homophily Features: This is the "homophily" metric calculated in the DFS manner. By doing a random walk from the task table, based on the retrieved con- text, we calculate how many target columns share the same label the seed ones. mean__saee catmeeeclssssaataaeeae wo.iaaiiiiinoreeeeeeo computes the average proportion of same-class neighbors while ignoring unlabeled nodes; ad mejustedmmann.same wee.nssae.claa......... sussstaaiii ratio provides a normalized version accounting for class imbalance; sparsity ratio quantifies the density of the relational graph; and mean past task nodes captures the average number of historical entities available for temporal tasks.
(4) Stats: We include log.total.rows computed as log(1 + train_rows + val_rows), which captures the logarithmic scale of the dataset. The log transformation ensures that the feature values are comparable across tasks with vastly different sizes.
Auto Transfer Features
We use 24 anchors in total. 12 for RDL and for DFS.
Model-Based Features It comprises eight performance indicators derived from lightweight model probes on the target task itself.
(1) TabPEN Features:
We compute tabpfn. hop and tabpfn.2hoo by evaluating TabPFN on 1 h-hop and 2-hop neighborhood aggregations of the relational features.
(2) Random Initialization Features: We include six features derived from randomly initial- ized graph neural networks: rfr randomsage 1, rfr randomsage 2, rfr ran randomsage 3, rfr ra randombbl feee 1, rfr randombbfnet 2, and ffr randomnb fnet. 3. These probes test whether the task's 's relational structure is inherently easy to exploit (even without learning), which can indicate task difficulty and the potential benefit sophisticated architectures.
E.3 DETAILED DESIGN SPACE
In this paper, we consider two classes of models: end-to-end learning (relational deep learning) models and non-parametric graph-based feature synthesis (DFS) models. Specifically, when the number propagation hops for the latter is 1, the corresponding model will be a relation-agnostic one. We then elaborate the module design inside each class.
RDL. Following wisdom in the design of the GNN architecture Youu et al. 2020 Luo et al. 2024),
modularize the RDL module into the following parts: feature encoding, (optional) structural feature, message passing module, (optional) architecture design tricks, readout function, hyperpa- rameters, and training objective. The design choices and rationales are detailed below.
For feature encoding, we stick to the ResNet-based encoder used in Robinson et (024).
2.
The structure feature particularly useful for link-level prediction, which aims to break the original symmetry of the GNN designed for node-level tasks. We consider learnable embedding and partial labeling tricks because of their effectiveness demonstrated in exist- ing benchmarks (Robinson et al. [024 Yuan al., 2024 Other features, such random embeddings, are neglected because of their limited effectiveness.
3. For message passing, we consider all alternatives used in the existing literature to study the correlation between the message passing function and task performance.
4. For architecture design tricks, we consider residual connection (He et al., (016) because of their effectiveness shown in Luo et al. (2024). However, under the RDB setting, find that these tricks do not always improve performance and result in much more computation overhead.
5. Readout is another important module in architectural design. For entity-level tasks, only consider MLP as the readout function. For link-level tasks, we consider ContextGNN and Shallow- Item (Yuan al. 2024), which integrates graph-free learnable embeddings
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Table Design space of unified architecture for end-to-end RDL models. "Grid" indicates that the module is selected from predefined grid, whereas "random" indicates that the module is randomly sampled.
Module name
Possible choices
Feature encoding
ResNet (Robinson et al. 2024)
Structural features (grid)
Learnable embeddings (for destination table, or for both
the source and destination tables) (Yuan et al. [2224] Ma et al.
2024); partial-labeling tricks from NBFNet (Zhu et al. 2021
Message passing (grid)
Sparse message passing: GraphSAGE (Robinson et al. 2024
Hamilton et al.
2017), HGT (Wang et al. 2024a| Hu et al.
2020b PNA (Corso et al. 2020 Wang et al. (024a));
sparse message passing with higher-order information:
Rel-
GNN (Chen al. (025a)
Architecture design trick (random)
Residual connections (Hé et al. 2016)
Readout (grid)
MLP, ContexIGNN, Shallow- Item Yuan al. 2024
Hyperparameters (random)
Learning rate, weight decay, batch size, dropout rate, num- ber of layers, hidden dimension, temporal sampling strategy, number of sampled neighbors
Training objective
Classification tasks: cross-entropy; regression tasks: MSE or MAE; link-level tasks: cross-entropy, BPR, margin-based losses
mitigate the pitfalls of GNNs link-level tasks. It should be mentioned that all pairwise methods like NCN (Wang et al. 2024b), SEAL (Zhang & Chen 2018) are not applicable because of the complexity.
6. Training objective is designed based common loss functions for different task formats.
The detailed syper-paramete search space is presented follows:
RDL SEARCH SPACE =
2
## these will through grid search
3
"full entities";
'pre 'pre_sf': sf' s'sre dst', 'zero learn', 'none'l "mpnn_type': ('relgnn', 'sage', 'hgt', 'pna'l 'post_sf'; ('link' ['none', 'shallow', 'contextgnn),, 'node' [' none' }}
5
6
7
}
"model config": {
9
"encoder num layers": [4], Storch_frame___ Sarraaeeeeeeeeeeeeeeeee___eeeeee codeel_css:: [' ['resnet'],
10
11
"batch_size"; [128, 256], "gnn_config";
# src: learnable embedding for src, dst: learnable embedding for are and dst,
14
"loss "loss_fn":
15
"binary classification"; ["bce"], "regression": ["mse", "mae"],
"recommendation"; ['bpr'l, I,
18
["ce"]
19
20
soudden_chaneels:: [64, 128, 256], "num heads": "num_heads"; [1, 4],
21
22
"dropout": [0.0, 0.5],
23
"norm": ("layernorm", "batchnorm", 'none'l, "aagreeation" ["mean", "sum"], jk is turned off because it almost always leads no performance gain with huge computation overhead "ik": [False], :orpp_connection: [True, False]
24
26
27
28
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29
"sampler config":
"temporal": ["uniform", "last"], "num_nee neum__eighhors: [32, 64, 128],
31
32
"num Saum_layers"s [1, 2, 3, 4]
33
"loader_type"s ["node", "edge"]
34
optimizer :
36
"Ir": [1e-3, 1e-4, 1e-2], "weight "weight_decay": [0.0, 1e-5],
37
38
"scheduler"; ("exponential
39
"gamma": [.8, .9, 1.]
}
41
}
42
E.4 GRAPH--NNDUCD NON-PARAMETRIC FEATURE SYNTHESIS MODEL
DFS. The graph-induced non-parametric feature synthesis approach follows a different paradigm compared to end-to-end RDL models. Instead of learning parameters through gradient descent, DFS models leverage graph topology and statistical aggregation to synthesize features. We modularize the DFS framework into the following components: feature aggregation strategy, propagation depth, model backbone, and hyperparameters. The design choices and rationales are detailed below.
Table 9: Design space of induced non- ma-paaametric feature synthesis (DFS) models. Grid means module selected from a predefined grid, while random means the module is randomly sampled.
Module name
Possible choices
Feature aggregation strategy (fixed) Propagation depth (grid)
Mean, sum, max, min, count, weighted mean, target encoding Number of hops (1 relation-agnostie). 2,
Downstream predictor (grid)
TabPFN (Hollmann et al. 2023). FT FT-Transformer, Light- GBM
Plyper-paramed (random)
Learning rate, weight decay, scheduler gamma, hidden di- mension, number of attention heads, normalization
Training objective
Classification tasks: cross-entropy loss; Regression tasks:
MSE or MAE
1. Feature aggregation strategies determine how information flows through the graph struc- ture. We follow Wang et al. (2024a) to adopt a fixed set aggregation strategies.
2. Propagation depth controls the scope of information aggregation. When the number of hops is 1, the model becomes relation-agnostic and only uses entity-level features.
3. We consider three typical types of downstream task predictors: tabular foundation model, gradient boosting tree, and neural networks.
4. Training objectives are task-dependent and align with the evaluation metries used in the benchmark datasets.
The detailed hyper- general, do not require many pyper-parameters, so model-related hyper-parameters are only applied to FT-Transformer.
hyper-paramen parameter search space is presented as follows: LightGBM and TabPFN, in
DFS SPS__SEACH_LRE SHEARCH_SPACE
2
## dds_laye: will go through a grid search
3
"dfs :ise_aayyr [1,2,3],
4
"model seadel__ype"; ["tabpfn", "ft_ transffrmrrr" "1gbm"], bbatch_size". [128, 256], mmoell_config"
5
7
## only for ft transformer saiddeensize"" [128, 256, 512), "dropout": [0.0, 0, 0.5], "num layers": [1, 2, 3, 4],
8
9
10
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11
"attn dropout": [0.0, 0.51, "num : [1, 4],
"normalization":
(" ("Layernorm", "batchnorm", 'none'
14
"loss_ "loss_fn";
15
"binary classificattonn: ["bce"],
16
"zegression": ["mse", "mae"],
"ranking": ('bce', 'bpr'l,
"multiclass htc::ccaaas_clsssfccaiiiioo ["ce"]
19
20
},
21
aptimizzerconnfi"" "Ir": [1e-3, 1e-4],
23
"weight_decay": [0.0, 1e-5],
24
"scheduler"; :"" ("exponential"]]
25
"ganma": [.8, .9, 1.1
27
E.5 SUPPLEMENTARY EXPERIMENTAL DETAILS
E.5.1 EXPERIMENTAL RESULTS FOR RECOMMENDATION TASKS
Here, discuss the recommendation tasks skipped in the main text. First, want to emphasize that the design space of recommendation tasks is much smaller since DFS- BES-based methods cannot work well recommendation tasks. The main reason is that the recommendation is more about capturing the collaborative signal across pairs of entities, which goes beyond the feature synthe- sis patterns of DFS. To make DFS work recommendation tasks, need to design common neighborhood--aate -aseed or path-based features, which makes it no longer "automatic" but requires sub- stantial feature engincering efforts.
In terms of RDL, we also need to emphasize that only a small portion of graph-related models can work under the RDB settings. Revisiting the traditional link prediction tasks OGB (Hu et al. (2020a), the positive and negative sample pairs are usually pre-defined, with negative samples coming only from small portion of the whole set. This makes possible to use pairwise models like NCN (Wang et al. (024b) and SEAL (Zhang & Chen, 2018). However, in RDB settings, the candidate set is usually the entire target table, which makes it impossible models. That's why NBFNet (Zhu et al. [021]. source-only model, is first considered in Yuan cet al. (2024). Such a scalability problem also affects the implementation of vanilla GNN. Unlike using link-level sampler in Wang al. (2024a), we have to use two node-level loaders, one for the source type and one for the target type, for representation computation. These properties make it only possible current stage.
use these pairwise
vanilla GNN, shallow embedding, NBFNet, combination of them in the
We then present the HPO experiments based these methods. As shown in Figure 5 the following phenomena:
observe
1. Overall, node-based loader dominates the link-based loader (or more accurately, the loader based on source and target types). One potential reason is that the node-based loader is closer to the idea of path-based retrieval, which is more effective in RDB recommendation tasks with rich relational paths. A potential exception the REL-AMAZON datasets, which we do not use here. For these kinds of datasets whose path patterns are super sparse, the path-based collaborative signal may live in distant neighbors. As result, on these tasks, we typically need neighbor loader with more than hops with dense neighbors to get good performance.
2. For the number of layers, unlike entity-level tasks, it presents that the deeper the better within certain range. This is because, for path-based retrieval, deeper layers can capture more distant signals.
3. For message passing designs, it is also somewhat different from the phenomenon entity- level tasks. Here, Relgnn presents clearly better performance. The reason is that there some tables with multiple foreign keys. Semantically, these tables are closer to an edge, while the PK-FK graphs, they treated as nodes. ReIGNN can simulate transforming these tables into
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hyperedges, and thus makes the model capture distant signals. HGT and PNA are better at capturing feature interactions, which are more important for entity-level tasks.
4. For structural features, partial labeling tricks of NBFNet more effective. One noteworthy phenomenon is that on many tasks, a node-level loader without any structural features can also deliver good performance. The reason is that the original features in the graph can act as implicit type embeddings, which makes multi-source path-based retrieval.
5. For readout functions, contextgnn does not always bring performance boost. However, it will not degrade performance either. This is also based on the sparsity of path patterns.
Takeaways for recommendation. Across RelBench- style recommendation tasks, ContextGNN a ro- bust default that delivers competitive performance with modest tuning. This largely reflects task properties: path-aware retrieval with shallow ID embeddings plus GNN context works well when collaborative signals are captured via multi-hop relational paths. On some other recommendation tasks on bipartite graphs, two-tower (dual-encoder) architectures may scale training and in- ference more effectively and simplify candidate genera- tion, though they typically require task-specific compo- nents (e.g., hard-negative mining, retrieval infrastruc- ture, and reranking) to reach top accuracy. Overall, these observations suggest that for recommendation, fully automatic architecture design may be less effective than crafting task-tailored consistnt with prevailing industrial practice.
Table 10:
Validation-seleeter and test-
selected performance gap. For RDL, we show top 2 architectures with the largest gap.
perf
perf gop
RDL Oveall HGT
76.75
(lpp))
72.77
74.95
DPS momall
16.90
75
1.63
TANFFN
75.91
75 25.70
0.21
Fi-Tnesfomeer
75.87
74.45
1.42
Method nome
Moan test rog perf
Mean val pert
Mean
RDL Oveull
5,9292
7.0578
0.1287
HGT (op1)
5.9407
7. 7.1035
0.1650
PNA (lo22)
7.1376
7.1795
0,0419
DES ovemil
3.6831
3.7155
0.0254
ToNe IN
3.7548
3.7092
Loader Distribution (Top from each task)
Numbar of Layers Distribution (Top 10 each task),
MPNN Type Distribution each task)
25
20
Count
Countt
10
5-
relgnd
Number Layers
Pre-SF Distribution
Post-SF Distribution
MPNN Type
Top 10 from each task)
(Top 10 from each task)
30
29
30
25-
Count
15
10
10
5.
none
scc d4
Coittatonn
nowe
Pre-SF Type
Post-SF Type
Figure 5: HPO results for recommendation tasks.
E.5.2 SUPPLEMENTARY EXPERIMENTAL RESULTS FOR THE MAIN TEXT
Full experimental results for Figure 2, The full result for Figure is presented in Table
Influence of micro-level design choices. Looking further into the influence of micro-level archi- tecture choices shown in Figure 6 we can observe that: (1) different design choices present in the top performing configurations, underscoring the importance of architecture design; (2) compared to
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Table 11: Full experimental results for Figure 2
BAGGN
KacakIM
RDL ollecs))
Gif
E0lan def
79.20
76.87
13.03
17:15
$2.41
13.33
54.35
77.30
7038
78.42
76.63
3131
7006
09/27
71:23
11135
7076
00.20
68.23
13335
05.6
03530
25334
03833
10823
03530
00803
03821
0.900
02960
01831
03822
28.11
36.17
1811
0961
14/0
75
3330
75.30
07.8
58.13
0.330
03391
0.280
11211
62540
02280
02307
0329
0,000
0.230
0.290
53136
08:33
08.30
0333
66.1
6611
03.1
5336
06.77
63.34
66.18
00.0
6333
71.24
10.8
71.15
71.41
08.15
10.00
18.15
0.300
33339
0480]
03970
040.0
03002
03952
0.328
0.910
0,586
83299
0376
older models like HGT and PNA, RelGNN does not present advantages in terms of prediction per- formance, which inspires us to revisits the wisdom of past research. (3) For RDL-based methods, learnable embeddings are mainly required to achieve top performance. (4) For DFS-based meth- ods, the FT- FT-Transformer works better for large-scale tasks, while TabPFN can well fit small-scale ones. Tree-based methods, such as LightGBM, are not optimal in cases; therefore, do consider them in the following text.
mpnn design
pre
distributions
10 (1)
10 (2)
10 (3)
Rank (lower is better)
8
Number Times
6-
6-
6-
2-
5ago
R.. frannssformer tabpfn
lightghm
Figure 6: Relationship between performance ranking and micro-level architecture choices. For RDL-based methods, we filter the original search result with the top 10 performing configurations on each task. For DFS-based methods, we filter the original search result with the top performing configurations on each task. The best configurations are selected based on test performance directly. "zero learn" the labeling trick adopted by NBFNet. (a) A violin plot of MPNN types shows that PNA achieves the best mean ranking across different tasks. (b) When comparing labeling tricks, although equivariant models appear more frequently in top rankings, a labeling trick still needed to achieve the top spot. (c) For DFS, ft. transformer and tabpfn present unique strengths, where the former can leverage more training samples, and the latter usually works better small-scale settings and can conduct in-context learning.
Performance gap between validation-selected and test-selected configurations. As shown in Table TO, we can see that both RDL and DFS suffer from this performance gap. Specifically, RDL presents much larger gap for regression tasks. TabPFN, the tabular foundation model utilizing in-context learning for inference, shows an advantage in mitigating such performance drift.
Analysis of afsed features. Here, show affinity-based features and empirical performance. For each task from the model performance bank, have three task-level anchor scores (TabPFN, RandomSAGE, RandomNBFNet and test performance for two model families (RDL, DFS). Anchors are constant within task; performances task-wise means over validated runs. For RDL also compare preprocessing options, none); define the per-task difference A RDL RRD,
empirical results the analysis of
pre_sf off.fee E (zero_leaae
(i) RDL DFS from TabPFN vs graphs. Using all tasks, a log-log fit shows
Iog(RDI./DFS) 0.091 0.262 log TabbFNNNNBFNN
(R² 1919).
when TabPEN exceeds the graph anchors, DFS tends outperform RDL; simple thresholds TabPEN/NBFNe > 1.10 or SabPEN/SAGE > 1.18 classified the winner at about 79% ac- euracy. (ii) RDL pre sf from graph-graph ratio. With R max(NBFNet)) max(SAGF). the
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linear association with A is small (Pearson -0.114, 19), but one-bit chooser it is useful: AUC(zero_leaxn better) s 0.718, and the rule R 2 0.977 = choose zero.learn (else none) attains 0.789 accuracy (base rate 0.684 favoring zero learn).
Visualization of loss landscape. Our first-step analysis to plot the loss landscape of a series of models presenting different val-selected and test-selected performance gaps. An example is shown in Figure We can see that the DFS, which generalizes better, presents a much flatter loss landscape.
FElrensformar Loss Lendscape
CNN Loss Landscape
(a) Loss landscape of DFS + Transfoommr on DRIVER-TON
(b) Loss landscape of RDL on DRIVER- TOP3
Figure 7: Loss landscapes
F.6 EFFICIENCY
In the main text, skip the discussion of efficiency-related concerns, such running time and memory consumption. One reason is that efficiency depends on the backbone implementation. For example, we implement SQL using in-memory databases in this work. One cannot say DFS or RDL is more efficient merely based on this implementation. In reality, DFS can potentially be accelerated via tools like Spark. Nonetheless, we still present efficiency-relatee main contents: (1) Average running time of RDL and DFS pipelines, which includes the time for model training and dataset pre-processing. (2) The way to extend our current methods to incorporate efficiency-related concerns.
results here, with the following
As shown in Table 12 we consider the running time of two representative tasks: driver-dnf and study-outcome. The former database is small-scale one, while the latter contains lots of columns. We consider the simple RDL models SAGE and the complicated ones HGT. For DFS, we consider three propagation depths: I, 2, and 3. We see that DFS is relatively more efficient when the propagation depth is small. The main backbone is just the feature encoder part of the RDL, so will be much faster during the inference stage. Moreover, without proposed PCA compression strategy, DFS is usually unusable for large-scale tasks. Generally, both RDL and DFS do not meet significant efficiency concerns.
Table Preprocessing and training times by method for driver-dnf and study-outcome.
RDC (SA0m)
EDI.CROT)
DES- on pip
DPS-2 too pio
DPS-3 Dao-3
RDI (SAGR)
RD.. (DC.)
DFS (ao
DES-2 me ply)
DPS- (oo
Pepprcccsing
7w12s
<1scc
<istc
To extend our current methods incorporate efficiency-related concerns, we consider the following two strategies: (1) Rule-of-thumb. Since we know the number of training samples, when the scale limited, then directly utilizing TabPFN and DFS is usually the most efficient approach. Moreover, for RDL, HGT is obviously the most expensive model considering its complicated attention mech- anism. (2) Joint optimization of efficiency and effectiveness. We can consider a multi-objective consider the validation performance and training time as two objectives. First, we can train a meta-model to predict the training time based on architecture designs. For example, list the following efficiency-related hyperparameters: number of lay-
optimization framework, where
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ers, hidden dimension, number of attention heads, and batch size. Then, train model whose input feature is the hyper-parameter configuration, and the output is the training time. To estimate the pre-processing time of DFS, it is approximately proportional the number of SQL operations multiplied by the size of training tables. We then demonstrate one formula to do joint optimization of efficiency and effectiveness.
(T* (T',0")
arg max EE (RDLL, DFS] SEST
(0 T
1 T DFS)
Cag- ops(
Tett
Mume (0, x) + Jame (8, T)
Teel
chooses the pipeline (RDL vs. DFS) and restricts the search space Sxi (0 π)) is pre- dicted validation effectiveness; À > 0 trades time for performance; Teer normalizes time; the DFS pre processing cost activated by 1[x and (0, π are the meta-modes 's mean and uncertainty for training time; 3 2 0 adds risk aver- sion to slow/uncertain runs. can be trained based on a model performance bank similar to the meta-predictor.
DFS] and modeled as Cagi ops(f) rows; Hame (0, π
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Reviews:
[{"review_id": "zHH0e2qjDh", "reviewer": "Reviewer_7kcR", "summary": "The paper focuses on automatic architectural selection for predictive task on relational databases. The model proposes Relatron, a task embedding-based selector using novel task signals. The performance bank is a reusable resource, and findings like task-dependent RDL vs. DFS trade-offs challenge prevailing assumptions in the field. Experiments demonstrate meaningful gains (up to 18.5% improvement with 10× compute savings), making it relevant for real-world deployment.", "questions": "1. How sensitive is Relatron to bank size? With fewer tasks, does transfer degrade?\n2. Could homophily be extended to link-level tasks (e.g., recs), and does it correlate with higher-order passing (RelGNN)?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The architecture search (180 configs/task for entity-level, 20 for DFS) is comprehensive. The findings are interesting as it show DFS wins on more tasks , attributing gaps to homophily.", "weaknesses": "1. The scope of the paper is limited, as it focuses on from-scratch models and defers foundation models(e.g., Griffin, KumoRFM) despite comparisons (Fig. 2). It would be interesting to see how Relatron can handle pretrained relational foundation models. \n2. The experiment dataset is limited, only covering most of RelBench and two additional tasks. It would be interesting to see the performance across multiple datasets. \n3. The correlation between homophily and RDL-DFS performance gains are strong, but non-parametric. It would be interesting to see if the authors can.generate synthetic data(tasks with varying homophily) to strengthen the claim.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WCadbFKxDm", "reviewer": "Reviewer_FPiy", "summary": "This work conduct a detailed analysis between Relational Deep Learning (RDL) and DFS method with heuristic feature aggregator. It reveals that 1) DFS can outperforms  RDL on some tasks. 2)Different architecture are needed for different RDB tasks. 3) valid performance is not reliable. So this work propose an automl framework", "questions": "1. While automl framework is useful, I still think a unified RDB foundation model is the future. Can observations in this work helps development of RDB foundation model?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Extensive experiments over various baselines and datasets. Figure 2, Table 2-4, shows various experiments.\n2. Insightful observation for model selection. To me, “Correlation between homophily and RDL-DFS performance gap\" is the most interesting finding. Though homophily is a common tool in graph learning, it is first used in RDB datasets to our best knowledge.", "weaknesses": "1. Griffin cited in this work should also be included as one important baseline on RDB tasks.\n2. The comparison between these auto ml framework and vanilla baseline is missing. If this framework leads to large computation overhead, then vanilla baseline may be prefered in real-world application.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mOxuiEKIlP", "reviewer": "Reviewer_Eebt", "summary": "This paper lays out a design space of modeling approaches for machine learning (ML) on relational database (RDB) tasks, and does a comprehensive benchmarking study on it. The findings suggest that different tasks require different design choices (including model architecture). To automate this choice the paper proposes a few metrics which have high correlation with the performance of various approaches on different tasks and can be used to predict the best method. This AutoML system (Relatron) achieves performance improvements over hyperparameter-tuning and auto-transfer baselines, while being more efficient computationally.", "questions": "1. Why is RDL better at low-homophily tasks and DFS better at high-homophily tasks?\n2. How can RDL be improved based on insights from 1?\n3. Are there some kind of error bars for Figure 2?\n4. What is \"labeling tricks for RDL\"?\n5. L412: What is GraphGym similarity? How is it \"ground truth\"?\n\nMinor:\n* L040: repeated citation\n* L059: remove space before footnote\n* L362-363: You might be interested in [1] as they have similar findings/methodology and propose similar terminology \"post-hoc selection\".\n\n[1] Post-Hoc Reversal: Are We Selecting Models Prematurely? NeurIPS 2024.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "* The paper is thorough in its parameterization of the design space.\n* The findings are interesting. I like the experiment and finding that validation metrics are unreliable as this is very important for temporal splits as in leading relational benchmarks.\n* The metrics proposed for \"task embeddings\" are simple, clear, clever, and have high predictive power.\n* The baseline comparisons are comprehensive.\n* The paper is well-written overall.", "weaknesses": "I think it is important to include intuitions and analysis (theoretical as well as empirical) for why RDL is better at low-homophily tasks and DFS is better at high-homophily tasks in the main paper. Currently it is in Appendix C.2, but it is too long (4 pages!) and it is not clear what the intuitive takeaways are. It would be nice to have a discussion of the main intuitions and takeaways in the main paper. It would be ideal if the theory can be substantiated with some experiments.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IjSDf6Hfsx", "reviewer": "Reviewer_Yptq", "summary": "This paper presents an empirical study comparing Relational Deep Learning (RDL) and Deep Feature Synthesis (DFS) for predictive modeling on relational databases. The authors construct a large-scale design space for both model families and build a \"model performance bank\" by running an architecture-centric search across numerous RDB tasks. Relatron uses diffferent signals in a meta-predictor to first choose between RDL and DFS (macro-selection) and then prune the search within that family.", "questions": "- The paper provides a theoretical argument in Appendix C.2 for RDL's strength in low-homophily settings. It would be better to explain the other, more surprising half: why DFS is so strong in high-homophily regimes. \n- Could Relatron also include KumoRFM, the current state-of-the-art foundation model, into its selection pool?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper targets an important problem, relational deep learning (RDL), in the machine learning.\n- The experimental analysis across models and datasets is interesting.", "weaknesses": "- The recommendation task is not involved. Only classification and regression task types from Relbench.\n- The results of a foundation model, KumoRFM, outperforms Relatron that selects between models. This questions the practical usage of Relatron. Since there is already a stronger foundation model, is it still necessary to design and train a model selector?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)