Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases
Authors: Md. Tanvir Alam
Md. Ahasanul Alam
Md Mahmudur Rahman
Md Mosaddek Khan
Venue: Accepted by TMLR ()

Abstract: Relational databases (RDBs) are ubiquitous in enterprise and real-world applications. Flattening the database poses challenges for deep learning models that rely on fixed-size input representations to capture relational semantics from the structured nature of relational data. Graph neural networks (GNNs) have been proposed to address this, but they often oversimplify relational structures by modeling all the tuples as monolithic nodes and ignoring intra-tuple associations. In this work, we propose a novel hypergraph-based framework, that we call rel-HNN, which models each unique attribute-value pair as a node and each tuple as a hyperedge, enabling the capture of fine-grained intra-tuple relationships. Our approach learns explicit multi-level representations across attribute-value, tuple, and table levels. To address the scalability challenges posed by large RDBs, we further introduce a split-parallel training algorithm that leverages multi-GPU execution for efficient hypergraph learning. Extensive experiments on real-world and benchmark datasets demonstrate that rel-HNN significantly outperforms existing methods in both classification and regression tasks. Moreover, although the benefits of split-parallel training diminish on smaller hypergraphs with fewer nodes due to communication overhead, it achieves substantial speedups of up to 3.18× on large-scale relational datasets and up to 2.94× on large hypergraph datasets.

Published in Transactions on Machine Learning Research (12/2025)
Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases
Md. Tanvir Alam
tanwir15@du.com
Deportment of Computer Science and Engineering University of Dhaka
Md. Ahasanul Alam
abasan@icse.du..ccb
Department Computer Science and Engineering University of Dhaka
Md Mahmudur Rahman
wahmaddur@cse.du.co
Department of Computer Science and Engineering University of Dhaka
Md Mosaddek Khan
mosaddek@du.ac..
Department of Computer Science and Engineering Universily of Dhaka
Reviewed on OpenReview: https: // openreview. net/ form?? id= 77VP
Abstract
Relational databases (RDBs) are ubiquitous in enterprise and real-world applications. Flat- tening the database poses challenges for deep learning models that rely on fixed-size input representations to capture relational semantics from the structured nature of relational data. Graph neural networks (GNNs) have been proposed to address this, but they often over- simplify relational structures by modeling all the tuples monolithic nodes and ignoring intra-tuple associations. In this work, we propose novel hypergraph-basee based framework, that we call rel-HNN, which models each unique altribule-value pair as a node and each tuple as a hyperedge, enabling the capture of fine-graine grained intra-tuple relationships. Our approach learns explicit multi-level representations across attributo-value, tuple, and table levels. To address the scalability challenges posed by large RDBs, we further introduce split-parallel training algorithm that. leverages multi-GPU execution for efficient hypergraph learning. Extensive experiments real-world and benchmark datasets demonstrate that rel-HNN significantly outperforms existing methods in both classification and regression lasks. Moreover, although the benefits of split-parallel training diminish on smaller hyper- graphs with fewer nodes due to communication overhead, it achieves substantial speedups of up to 3.18x on large-scale relational datasets and up to 2.94x on large hypergraph datasets.
1 Introduction
Relational databases (RDBs) are among the most widely used forms of data representation in enterprise environments, owing to their ability to efficiently handle structured data, support complex queries, and maintain data integrity. A relational database consists of multiple tables governed by schema that defines the relationships among them. RDBs serve as the primary data format across a wide range of industries, in- cluding online advertising, recommender systems, healthcare, and fraud detection. Despite their widespread use, the dircct application of machine has received limited attention from the research community. Traditionally, applying machine learning to re- lational databases requires transforming the data into flat, tabular format, since most supervised learning
deep learning to relational databascs (RDBs)

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models rely on fixed-size imput vectors. This transformation process, commonly referred to as flattening, typically involves joining multiple related tables into single denormalized table with predefined columns. Flattening usually demands extensive, rule-based feature engineering (Anderson et al. 2013 Bahnsen et al. 2016 Covington et al., 2016), and frequently results in the loss of valuable relational information embedded within the schema and data. Moreover, for large-scale RDBs, flattening introdecess substantial computations overhead, becoming a major bottleneck in the overall machine learning pipeline. Consequently, there is an increasing demand for machine learning approaches capable of directly operating on relational data in its native form, without the need for manual feature engineering flattening.
Effective application of neural network-based techniques to relational databases hinges on overcoming two fundamental challenges. First, the inherently structured yet complex nature of relational databases, charac- terized by multiple interconnected tables, demands models capable of capturing and leveraging these intricate relationships. Second, the considerable scale of relational databases- often containing millions or even bil- lions of records across dozens to hundreds of tables necessitates officient training and inference procodures to ensure both practicality and scalability (Hilprecht, al. 2023). A recent approach that has gained trac- tion to address these challenges involves applying Graph Neural Networks (GNNs) directly to relational
databases LLi ct al. 2016 Hamilton ct al.] 2017 Kipf & Welling 2017: Velickovic et al.) 2018 Xu et al.
2019 Cvitkovic 2020b). In this context, the primary task typically involves predicting values for a target column in a specific table, using available relational information from the entire database. To achieve this, databases are modeled as graphs, where tuples serve as vertices and relationships between tuples from differ- ent tables-defined by foreign keys serve as edges. To better capture relational semantics, existing methods have proposed utilizing reladion--ype-depeedeett weights (Schlichtkrull et al. 2018), ; specialized convolutionn
operators (Huang ct al. 2020), or gencrative architectures (Sun et al. 2019) within relational graphs.
Despite their expressive power, existing GNN-based approaches have notable limitations when modeling relational databases. Primarily, they treat entire tuples as monolithic nodes, thus ignoring the granular, attribute-level structures and failing to capture fine-grained inter-attribute interactions. Morcover, rely- ing solely on primary foreigu key (PK FK) relationships significantly limits their ability to represent meaningful relationships among tuples within the same table. Additionally, these methods often overlook symmetries among sub-graphs and require multiple rounds of message passing, resulting in inefficient training and inference processes, particularly at scale. Furthermore, existing methods are heavily schema-dependent, making them challenging to generalize or adapt to new datasets wouh substantial human effort to define or extract PK FK constraints explicitly.
An emerging alternative is sypergraph-basee modeling, which has recently shown promise for automated learning on relational databases (Bai et al. 202T). A hypergraph generalizes traditional graphs by allow- ing edges (termed hyperedges) to connect an arbitrary number of vertices rather than being restricted to pairs. A hypergraph can reveal complex structural patterns involving multiple vertices and provide insights into network dynamics- paateerss that traditional graphs, limited to pairwise connections, fail to capture (Kim et al. 2024). This ability to represent higher-order interactions among vertices has gained significant attention across various real-world complex systems, including physical systems (Battiston & Petri 2022), microbial communities (Morin et al. 2022), , brain functions (Expert & Petri 2022] and social networks (la- copini et al. 2022). The flexibility of hypergraphs in modeling multi-way interactions has motivated the development of powerful hypergraph neural network (HGNN) algorithms (Feng et al., 2019 Chien et al.,
2022 Yadati et al. 2019), facilitating the learning of intricate relational patterns.
For relational databases specifically, ATJ-Net (Bai et al. 2021 leverages hypergraph to train a heteroge- neous GNN. It initially represents joinable attributes as vertices and tuples as hyperedges. The hypergraph is then transformed into to a heterogenous bipartite graph where the tuples and joinable attributes constitute vertices. Then, it applies a message-passing GNN to the bipartite graph to predict labels associated with tuples in the target table. Although ATJ-Net includes joinable attributes alongside tuples, it has several critical limitations. First, it considers only categorical, joinabl attributes toriiclls-yyiilllyllyyl those defined explicitly through primary key-foreign key (PK-FK) tional paths and potentially missing complex attribute associations within tuples. Second, transforming the hypergraph into a bipartite graph inevitably flattens high-order relationships into pairwise edges, causing the
cestonships-hhreeee confining the model to predefined rela-
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loss of valuable higher-order interactions that could otherwise be effectively captured by hypergraph neural networks. Based on the above discussion, our work makes the following key contributions:
We propose a novel hypergraph representation for relational data that preserves both intra-tuple and inter-tuple relationships. Unlike traditional methods relying exclusively on primary key foreign key (PK-FK) constraints, our representation decomposes tuples into attribute-valle pairs, creating nodes naturally connected via hyperedges. This approach effectively captures fine-grained attribute- level interactions and is inherently schema-ngnostie, climinating the need for manual feature engi- neering or explicit schema knowledge. To the best of our knowledge, this is the first hypergraph--based representation specifically developed for relational database learning.
We introduce rel-HNN,, hypergraph neural network specifically tailored for relational databases. Leveraging our hypergraph structure, rel-HNN learns explicit embeddings at three gramuarity lev- els attribute value pairs, tuples, and entire tables thus effectively capturing both localized and global relational patterns and enabling richer relational learning.
Additionally, to overcome the challenges posed by largo-scale relational databases, we propose a split- parallel hypergraph learning algorithm that leverages multi-GPU parallelism. Our method enables full-hypergraph training by partitioning both data and computation across GPUs while preserving global structural context. Unlike mini-batch GNN training, which introduces redundant data move- ment and overlooks neighborhood completeness, our approach ensures efficient and context-aware learning. To the best of our knowledge, this is the first work to introduce split-parallclism for scalable training of hypergraph neural networks.
Finally, our extensive experimental results demonstrate that the proposed multi-level representa- lion framework enables rel-HNN to significantly outperform the state-of-the-art methods on both classification and regression tasks across a diverse set of real-world relational datasets. Moreover, our split-parallel training framework delivers substantial performance improvements on largo-scale relational datasets and large beuchmark hypergraph datasets, achieving speedups of up to 3.18x and 2.94x, respectively, although the benefits of parallelization diminish for smaller datasets due communication overhead. These results highlight the effectiveness and scalability of our approach.
The remainder of the paper is organized as follows. Section 2 introduces the preliminaries and formally defines the problem. Section 3 reviews relevant related work. Section 4 details the proposed methodology. Section 5 presents the experimental setup, results, and analysis. Finally, Section 6 concludes the paper and outlines potential directions for future work.
2 Background
A relational database (RDB) is defined as a collection of tables, denoted by RDB = where each T E RDB represents a table. Each table captures information about a specific entity type, with rows (tuples) corresponding to entity instances and columns representing attributes or fcatures. Table columns may contain diverse data lypes, including numerical values, categorical text, timestamps, geographic coordinates, and multimedia content. We denote the i-th row of table I as T.k and the j-th column as T
Let Atl denote the set of attributes (i.e., columns) of table TK An RDB is referred to as "relational" because values in a column 7. of one table may refer to rows in another table 7 E RDB. Such columns are known as foreign keys and serve as the basis for modeling inter-table relationships Learning tasks on a relational database (RDB) are typically formulated as predicting the values of a specific column in a designated target table. Let The E RDB be the target table. In the training data, the rows of table Tht in the training set are associated with label. The goal is to predict the labels for the remaining rows of table T where the label values are unknown. A hypergraph is generalization of traditional graph that can be represented as H (V, E, E),,, where V {U1, u2,..., tlde is the set of nodes vertices, E- fer.e2..... .a,,ee,..,,ppp } denotes the set of hyperedges, and X E IgIV the feature matrix. Each hyperedge e, V includes the set of nodes it connects.
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3 Related Works
In this section, we review rescarch closely related to our work. Soction 3.1 discusscs graph-based learning approaches for relational data, and Section 3.2 covers distributed multi-GPU training strategies for scalable graph and hypergraph learning.
3.1 Learning on RDB using Graphs
Graphs are well-suited for representing structured data, as they naturally capture relationships and de- pendencies among entities. Leveraging this property, graph neural networks (GNNs) have been applied to tabular data learning (Li et al. 2025). Here, the data instances (rows) are represented as nodes, and edges are formed based on similarities between instances using their feature vectors, capturing correlations between samples (Errica 2023 In another line of work, each feature (column) is modeled as a node, and edges encode correlations or dependencies between features, which can be derived from statistical measures or learned feature embeddings (Zheng et al.] 2023). The ability of GNNs to capture complex relational dependencies makes them a strong candidate for modeling relational data beyond tabular settings. Accord- ingly, GNNs have been increasingly adopted in relational database systems-related tasks (Li et al., 2025) including performance prediction (Zhou et al. 2020), query optimization (Song et al. 2022 , and cardinality estimation (Chronis et al.) 2024). Beyond these system-level tasks, GNNs have also been employed for su- pervised learning on relational databases (Cvitkovic 2020a), where the database is represented as graph to predict larget attributes or classify tuples while preserving the underlying relational structure. A common approach for relational data in machine learning is to 'flatten" relational data into a single table format, as most widely used supervised lcarning models require inputs in the form of fixed-size vectors. However, this flattening process often eliminates valuable relational information inherent in the data. The relational database transformed into a graph, representing the relational structure, to predict the target attribute. In the graph representation, each node corresponds to tuple, and edges represent foreign key relationships between these tuples. To manage the complexity of the graph, connections are typically restricted to a specific depth or number of hops. Finally, a GNN on this graph is applied to predict the target altribute using the GNN. A complete mapping between relational database concepts and graph terminology is pro- vided in Appendix [A] Representing a relational database (RDB) as a graph enables supervised learning tasks on RDBs to be framed as node classification problems (Atwood & Towsley 2016). This representa- tion supports both classification and regression tasks. The graph-based perspective also suggests that Graph Neural Network (GNN) techniques can be effectively applicd to learning tasks involving relational databases. GNNs, particularly those designed for supervised learning on RDBs, commonly utilize the message passing framework Giimer et al.] 2017). In this framework, each node v in a graph is initially assigned hidden reprosentation hg, which is itcratively updated over R rounds of message passing. In each round r, node v transmits a message m to each of its neighboring nodes w. These messages are generated by a learnable function that may incorporate edge features and depend on the current hidden states hū and hn of the source and target nodes, respectively. Each node then aggregates the messages received from its neighbors using another learnable function, resulting in an updated hidden state hC*1. After R rounds of message passing, a readout. function aggregates the final hidden states h across all nodes to produce a prediction for the entire graph. Learning with GNNs typically requires multiple message passing among nodes, which leads to significantly high training and inference times. To address this limitation, a recent approach named SPARE (Hilprecht et al.) 2023) introduces an alternative encoding technique that represents the RDB as a directed acyelic graph (DAG). This DAG constructed by performing a breadth-first search (BFS) on the undirected graph G, rooted at a target tuple E 7 Edges are directed from nodes at greater depth to nodes at lesser depth. This DAG structure enables single-pass learning, allowing for faster training and inference compared to standard GNNs. More recently, ReIGT (Dwivedi et al.) 2025 proposes graph transformer architecture tailored for relational databases, capturing relational heterogencity and long-range dependencies through multi-elemnent tokenization and local global attention. Both GNN- and DAG-based methods model relational data as standard graphs, often overlooking structural regularities defined by the databasc schema. This can lead to rodundant computations and reduced learning efficiency. In contrast, a more recent approach, ATJ-Net, introduces a hypergraph- based representation that enables more adaptable and automated learning across relational databases (Bai et al.) 2021
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Following the success of graph neural networks, hypergraph neural network models have received growing attention. HGNN (Feng et al. 2019) and HyperGCN (Yadati et al. 2019) extend graph convolutional networks to the hypergraph setting, while AllSet (Chien et al. 2022) introduces two-stage wesssae-ppsssnng framework. In this approach, hyperedge representations are first computed by aggregating the embeddings of their constituent nodes from the previous layer; then, the embeddings of the hyperedges connected to a node are aggregated to update that node's representation. Similar to graph-based approaches, rescarch has been conducted on hypergraph hypergraph-enhanced tabular representation learning model in which each cell of table is represented as a node, while rows, columns, and the entire table are modeled as distinct hyperedges. This design enables HYTREL to capture higher-order interactions among table components and preserve structural invariances. However, HYTREL primarily focuses on intra-table dependencies and does not model relational dependencies, making it unsuitable for complex relational databases. Furthermore, the substantial memory and computational overhead introducod by its iitentioo-basek hypergraph transformer, combined with the sparsily of the constructed hypergraph, limits its scalability to large tables. ATJ-Net (Bai al.] (022T) models the relational database RDB as hypergraph, each tuple T of table T RDBB is considered as jk, where T denotes the i - th tuple of table T E RDB. Joinable attributes the
tabular data learning also. HYTREL (Chen et al.] 2023) is a
hyperedge, that is E' JT, serve as the vertices, and other attributes are treated as features of the hyperedges. Every main table- must include at least. one joinable attribute; otherwise, it cannot be linked to the target label. If a hyperodge contains a vertex, a connection is established between them. Formally, let Attr (f) be the value set of - th joinable attribute of table 2 and V {Attr"* (i)3x.. Moreover, the hypergraph can be viewed as bipartite graph, with tuples and joinable attributes represented as two distinet sets of vertices, and edges indicating their inclusion relat ionshiss ATJ-Net employs the message passing neural network (MPNN) framework |Gilmer et al.] 2017) to construct GNN over the heterogeneous hypergraph. In each GNN layer, hyperedge features are first aggregated to the vertices, which are then updated and propagated back to the hyperedges. Although ATJ-Net uses a hypergraph-basde formulation, it ultimately trains a GNN by stacking standard GNN layers, which limits its capacity to fully exploit the rich relational structures across tables in RDB. Additionally, it considers only joinable attributes and neglects the representation learning of other attributes, potentially reducing its effectiveness in modeling complex intra-tuple associations.
3.2 Distributed multi-GPU GNN training
The challenge of scaling to large graphs has led to extensive rescarch on multi-GPU GNN training systems. Training strategies for utilizing multiple GPUs have been devised for both mini-batch and full-graph training (Wang et al. 2022 Wan et al. 2023). In mini-batch multi-GPU training, nodes are first sampled into micro- batches, one for each GPU. Then, the local k-hop neighborhoods of the target nodes in each micro-batch are sampled and loaded. Each GPU subsequently trains the model on its assigned micro-batee in a data data-paralled fashion (Wang et al., 2019: Gandhi & Tyer 2021). However, data-parallel training often results in redundant data loading and computation due to overlapping k-hop neighborhoods across micro-batches. To mitigate this issue, GSplit (Polisetty et al. 2025) partitions the mini-batch into non-overlapping splits, assigning each split to specific GPU. Each GPU processes only the vertices in its assigned split and exchanges intermediate results at each GNN layer. Nonetheless, mini-batch GNN training relies on neighborhood sampling, which may exclude important neighbors, leading to information loss and suboptimal message propagation. Full- graph multi-GPU training (Wang et al.) 2022 Wan et al. 2023), in contrast, maintains the complete graph structure during training. G3 (Wan et al. 2023) splits the full graph into non-overlapping partitions, with each GPU responsible for one partition. After cach layer, GPUs exchange outputs with other GPUs that require them for the next. round of computation. While these methods are effective for large-scale graphs, they are not directly compatible with hypergraphs.
4 Proposed Methods
In this section, we present our proposed algorithms for learning from relational databases. We begin by constructing a hypergraph representation of the given relational database, which preserves fino-grained rela- tionships and facilitates hypergraph neural network learning (Section 4.1p. After transforming the relational database into hypergraph, we apply our proposed model, rel-HNN, which captures complex relationships
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Figure 1: An example hypergraph generation for a relational database
multiple levels of granularity (Section 4.2 Finally, in Section 4.3 we introduce our split-parallel hypergraph
learning algorithm designed scale effectively to large relational databases.
4.1 Relational Database to Hypergraph
We now detail our hypergraph construction approach for relational databases, which enables hypergraph neural network learning by capturing complex, fine-grained intra-tuple relationships beyond conventional primary key-forcign key constraints. Our approach dccomposcs cach tuple in the RDB into attribute-value pairs. Instead of representing a tuple or row as a node as existing GNN-based approaches, we create a node for each unique attributo-value pair, (Attr), TH3,,, found in all the tables contained by the database. Then, for each table 7 E RDB, for each row T.E we create hyperedge that connects the nodes associated with the attribute-value pairs, (Attr), Thy), contained by the row. This hypergraph formulation provides natural way to model fine-grained interactions within luples, moving beyond rigid primary fer-foreign key constraints, enabling the learning of richer representations. Figure illustrates a hypergraph representation for a relational database, where attribute-valut pairs are represented as nodes. Edges are shown as colored circles, each connecting more than two nodes to form a hyperedge. For the feature vector X, of a node E V, we consider two techniques. In the first approach, we assign one-hot encoded vectors as feature representations for the nodes. In the second approach, we construct the feature vector such that each index corresponds to either attribute from the tables or value present in the tables. For each node, we initialize a zero vector and set the indices corresponding to its associated attribute and value to 1. The detailed construction procedure is provided in Algorithm I in Appendix C
4.2 rel-HNN: Hypergraph Neural Network for Relational data
Building the hypergraph construction described in the previous subscction, our proposed hypergraph learning algorithm designed to operate the resulting hypergraph derived from relational database. Rel-HNN adopts a two-phase pussage-passing mechanism (Chien et al. 2022) that alternates between aggregating information from nodes to hyperedges and then from hyperedges back to nodes. In each layer, for hyperedge embedding, messages (i.e., embeddings) are aggregated from the nodes it connects. Similarly, each node updates its representation by receiving messages from the hyperedges which belongs. These two phascs naturally capture the semantics of relational joins: the first phase aggregates attribute-level information within each relation, while the second phase propagates the relational context back to the corresponding attribute-valut pairs. In addition the prior two-phase hypergraph learning techniques, rel-HNN introduces relation-aware, table-conditioned aggregation mechanism. We integrate learnable table embeddings and relation-specific transformations, which enable rel-HNN to jointly capture structural dependencies and relational semantics across heterogeneous hyperedges within relational databases. Figure illustrates the architecture of rel- HNN as applied to the hypergraph depicted in Figure
present rel-HNN,
At the initial layer (layer 0), we compute the embedding Zg of node E V by applying a multilayer perceptron MT.PO to its input feature vector X,, that is 7 (TTPP((.) Learning node embeddings that

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represent attribute-value pairs allows the model to capture fine-grained semantic relationships within tuples, resulting in more expressive representations of relational data. Subsequently, for each hyperedge e E E, we determine its intermediate embedding at the initial layer, denoted as Fe, by applying another multilayer perceptron to the sum of the initial node embeddings it connects, as defined in Equation !
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In Figure 2 the imitial embedding es of edge e4 is determined by applying the multilayer perceptron the sum of the initial embeddings of node Vg and node Vusery i.e, F4 M L Vaga:2 + Veoeer o.). as edge ед is connected to these two nodes. By aggregating node embeddings to learn hyperedge embeddings that represent tuples, the model encodes higher-order interactions and co-occurrence patterns among attribute value pairs, providing comprehensive understanding tuple-level semantics.
For each hyperedge e E, we learn its final embedding at loyer by concatenating its intermediate embedding D0 with Zr., the embedding of the table that contains the row corresponding to the hyperedge, which is - CO Zr.). In Figure 2 the final embodding of odge e4 at layer 0, e4,4, is obtained by concalenating the initial embedding of e4, e4, with the table embedding of table TUser, e4 originates from the Uee table; ie., e4 F F In our model, learning table-level embeddings enables the incorporation of global patterns shared across all tuples within a table
For the intermediate layers (layer 1 to layer L), we determine the embedding of a node v, Z by aggregating the hyperedge embeddings from the previous layer (Equation 2). Here, MLP pf is the multi- layer perceptron for nodes at layer I and & is the set of hyperodges in E that contains
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For each hyperedge e corresponding to row the target table T the final embedding zt, where the last layer, represents the predicted class probability. In Figure 2 the hyperedges e1, E2, and e3 correspond to rows of the target table Trdder. The pscudocode of the rel-HNN algorithm is provided in Algorithm in Appendix C
4.3 Split-Parallel Hypergraph Learning for large databases
To enable scalable learning large hypergraphs derived from real-world relational databases, we propose split-parallel hypergraph neural network algorithm. In practice, relational databases tend to be large, and converting them into hypergraphs results in a substantial number of nodes and hyperedges. In particular, assigning a node to each altribute value pair significantly increases the number of nodes, especially in sparse datasets. Learning on such large hypergraphs introduces critical challenges in terms of computational runtime and memory usago, often making infcasible load the entire hypergraph into the limited memory of a single GPU. To address these limitations, we partition the hypergraph by dividing the node set into disjoint splits and distribute the computation of each split and its incident hyperedges across multiple GPUs. This

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strategy effectively mitigates both memory and runtime bottlenecks, making the training process practical for large-scale relational databases. However, learning from node-partitioned hypergraphs is non-trivial, hyperedge embeddings rely aggregating node embeddings that may be distributed across multiple GPUs, leading to communication overheads and synchronization challenges during training.
To utilize parallel processing, assuming there are GPUs available, we divide the nodes V in the hypergraph = (V, E) into N partitions, V1, V22 (V, E, into N partitions, Hi = E,, Here, E, {(ulv e and E ViHle E E and n Vi # 0}. Given N GPUs (GPU1, GPU2., and GPUx), we load the hypergraph H, into GPU, Now, in each GPU, GPU, in parallel, determine the embedding of a node E V, at layer 0, Ze. by applying the same multilayer perceptron layer M to the feature vector X,, similar to rel-HNN. The pseudocode of our proposed split parallel Hypergraph Neural Network algorithm is provided in Algorithm 3 in Appendix C In Figure 3 we demonstrate an example of parallel lcarning using two GPUs (GPU-1 and GPU-2). We divide the nodes in two partition, node Vi = (VAge:25; VAgea7, VUser DD:1 DD., VUser]ID:2: VAddr:n) and V22 = {VAddr:A, Vitemet, Vitem:2, Vprice15). The local computations are visualized the blue-shaded area for GPU, and the red-shaded area for GPUz. For each node, the embedding layer 0 is learned locally on the assigned GPU, For example, the embedding for VAge:25 is learned in GPU-1.
and VN- Following the partition, we split the hypergraph
H2 = (V2, Ez, X2),
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In rel-HNN, apply the multilayer perceptron layer, MLPS, to the sum of node embeddings to determine the intermediate embedding at the initial layer, E (Equation D For split learning, we divide L into two components, consisting of a linear transformation MLP Es followed by a non-linear transformation function MLPP Let the original MLP consist of I. stacked layers defined recursively as H (x) = a (W, W b,), H2(x) = (W,H,, (x) + b2),.... HH(x)) oz (W,Hz-11z) + bz). Then, MLP Hz(x). For split- parallel learning, the first. linear mapping can be applied independently to the local embeddings on each GPU, which is M ML E, (x) Wix. The subsequent transformations and activation functions grouped into MLP8 (x) WW ... (2 (a1(x xx + b1)) + ... by-1) bz), which is applied after the local linear projections aggregated across GPUs. A justification of this decomposition is provided
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We compute the local hyperedge embeddings at the initial layer associated with the i-th partition, denoted as Fe, by applying M MLPE to the sum of the node embeddings at layer 0 (Equation
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(4)
Bc
WE
In Figure 3 the local hyperedge embedding at layer 0 for hyperedge at GPU-1, Fo is -earned from Vuser ID:1 _D:1 and VAdde:B, the constituent nodes in the partition. On the other hand, for GPU-2, F is learned from Vitmm1
To learn the global hyperedge embeddings at the initial layer, Fo, we determine the sum of the local embeddings and apply the non-linear activation function MT.P (Fquation 5). This aggregation of local embeddings from different GPUs requires inter-GPU message passing. We allocate a global tensor with El rows with the same global ordering of edges on every GPU. Each GPU fills its entries for edges in its partition with partial vectors (zeros elsewhere) and then performs an AllReduce (sum) operation across GPUs to obtain the global embeddings.
F = MLP
Σ P
(5)
Finally, for each hyperedge e E, we learn its final embedding at layer 0, Ze, by concatenating F with Zr, the embedding of the corresponding table. In Figure 3 the final embedding of at layer 0, 2 is learned by aggregating their local embeddings at GPU-1 and GPU-2 and then concatenating their corresponding table embedding, Zporder.
For the intermediate layers, we determine the embodding of node V,, Z using Equation par- allel. The process of determining node embeddings requires no inter-GPU messaging as the corresponding hyperedge embeddings already accumulated in the assigned GPU. For example, in Figure the embed-

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ding of node VUser ID:1 at layer 1, Ve is calculated by aggregating Za, and Z which already accumulated in GPU-1, For the hyperedge embeddings, similar to the initial layer, we divide MLPF. as a single hidden layer MLP consisting of one linear transformation, MLPE followed by remaining non-lincar transformations MLP, o- We determine the local hyperedge embeddings at the initial layer associated with the i-th partition, F2, by applying M LP on the sun of the embeddings of the nodes from the same layer (Equation 6
F MLPE Ec
Σ % VEE
(6)
Finally, we learn the global hyperedge embeddings at layer Zz we determine the sum of the local embed- dings and apply the non-linear activation function MLPE (Equation 7
Z = MLP.E. E
F
(7)
1=0
5 Empirical Evaluation
In this section, we present a comprehensive set of experiments focused on finding the effectiveness and performance gain of our proposed rel-HNN approach. The structure of this section is as follows: we begin by detailing the datasets used and addressing model architecture, along with the experimental settings. Next, we separately demonstrate the performance improvements achieved by our approach on both classification and regression tasks. Lastly, analyze the effectiveness of our split parallel hypergraph learning approach on both relational and hypergraph datasets.
5.1 Experiment Design
To verify the effectiveness of our proposed rel-HNN model, we conducted experiments on both classification and regression tasks. We compared its performance against state-of-the-art graph-based algorithms for learning on relational data. Specifically, we applied GCN (Kipf & Welling 2017) and GAT (Velickovic et al. 2018) on graphs constructed from tuples connected through primary foreig key (PK-FK) relationships. For SPARE (Hilprecht et al.) 2023), we considered both GCN- and GAT-based variants, referred to as SPARE-GCN and SPARE-GAM GAT- respectivcly. For hypergraph neural networks, we have included Hyper GCN Feng et al. 2019) : HGNN (Yadati et al. 2019 and DPHGNN (Saxena al.) 2024). We also evaluated ATJ-Net (Bai et al. 2021h, which leverages heterogencous GNN architecture. We experimented with RelGT (Dwivedi et al. 20255, graph transformer architecture developed specifically for relational databases In our experiments, we included four versions of rel-HNN. Rel-HNN-one uses one-hot encoding, while rel- HNN-av uses attribute - value encoding for node features. In both rel-HNN-one and rel-HNN-av, table embeddings are omitted. In contrast, rel-HNN-ono-t and rel-HNN-av-t include lcarnable table embeddings in their respective architectures. For all the rel-HNN variants, we have set. the number of layers L, 2. The embedding length of all the nodes and hyperedges is fixed two. For rel-HNN-one-t and -N---Nt- the table embedding dimension is set to 8 across all the datascts. We have adopted stratified 5-fold cross- validation to preserve class distribution. All experiments were conducted on a workstation equipped with an Intel Core 17-7700 CPU @ 3.60GHz, 48GB RAM, and four NVIDIA GeForce RTX 3060 Ti GPUs with 8GB of memory each. To validate the applicability in real relational database environments, we developed an end-to-end pipeline connecting a PostgreSQL database to the rel-HNN training framework also. The pipeline extracts tuples and relationships using weigtt SQL queries, transforms them into hypergraph representations without requiring costly database operations as joins.
5.2 Performance on Classification Tasks
Table presents the AUROC scores for different methods evaluated across the nine real-life datascts (sce Appendix E for detailed dataset descriptions). The results clearly show that the rel-HNN variants consis- tently outperform the baseline methods with the highest AUROC score nine out of nine datasets while
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maintaining comparable lower standard deviations, indicating stable performance. Among the state-of- the-art methods, RelGT performs the best on six out of the nine datasets. On the remaining three datasets, ATJ-net performs the best. However, on datasets such as SameGen, and rel-fl (dnf), ATJ--eet exhibits comparatively lower performance than the GCN, GAT, and SPARE variants. Among the GNN-based ap- proaches, SPARE variants generally achieved a slightly higher AUROC score compared to GCN and GAT. These graph-based methods model tuples as monolithic nodes and primarily rely on primary for-foreign key relationships, which limits their ability to capture fine-grained relational dependencies. Within the class of hypergraph neural network-based methods, DPHGNN outperforms both HyperGCN and HGNN. How- ever, these models are designed for generic hypergraphs and treat hyperedges as homogeneous sets, lacking explicit relational or table-level semantics. In addition, HyperGCN relies on graph-based approximations of hyperedges, which limits the modeling of higher-order interactions, while HGNN and DPHGNN employ fixed aggregation schemes that ignore varying hyperedge roles.
Performance comparison of methods across datascts for classification tasks. Mcan AUROC is shown with slandard deviation in heses; bold values indicate the best performance.
Table 1:
rel-fl
rel-f1
rel-avito
Method
Hepa
Pima
Cora
SameGen
IMDB
Mutag
(top3)
(duf)
(uv)
0.5484
0.5438
0.5365
0.5245
0.6621
0.6357
0.5582
0.5122
0.4751
GCN
(0.0820)
(0.0746)
(0.0502)
(0.0918)
(0.0854)
(0.0650)
(0.0192)
(0.0837)
(0.0531)
0.5451
0.5442
0.5393
0.5119
0.6632
0.6320
0.5589
0.5064
0.1801
GAT
(0.0465)
(0.0746)
(0.0529)
(0.0509)
(0.0364)
(0.0683)
(0.0283)
(0.0928)
(0.0850)
0.5636
0.5511
0.5712
0.5233
0.6718
0.6044
0.5657
0.5241
0.5085
SPARE-GON
(0.0374)
(0.0374)
(0.0650)
(0.0465)
(0.0827)
(0.0912)
(0.0928)
(0.0918)
(0.0537)
0.5594
0.5527
0.5679
0.5276
0.6865
0.6271
0.5323
0.5222
0.5101
SPARE-GAT
(0.0465)
(0.0509)
(0.0370)
(0.0746)
(0.0991)
(0.0928)
(0.0501)
(0.0703)
(0.0608)
0.5075
0.4983
0.4833
0.5134
0.7348
0.5832
0.5213
0,4923
0.5056
HyperGCN
(0.0426)
(0.0394)
(0.0364)
(0.0401)
(0.0273)
(0.0323)
(0.0273)
(0.0099)
(0.0679)
0.4997
0.5032
0.4819
0.5087
0.7483
0.5783
0.5012
0.4753
0.5148
HGNN
(0.0375)
(0.0021)
(0.0178)
(0.0628)
(0.0195)
(0.0279)
(0.0933)
(0.1028)
(0.0828)
0.5482
0.5238
0.5247
0.5439
0.7526
0.5915
0.5236
0,5023
0.5313
DPHGNN
(0.0568)
(0.0546)
(0.0808)
(0.0528)
(0.0183)
(0.0839)
(0.0274)
(0.0368)
(0.0916)
0.5950
0.6072
0.6242
0.5030
0.8575
0.8812
0.6412
0.5030
0.5625
ATJ-net
(0.0444)
(0.0314)
(0.0111)
(0.0078)
(0.0273)
(0.0505)
(0.0081)
(0.0078)
(0.0942)
0.6643
0.5839
0.6513
0.6464
0.8450
0.7215
0.8234
0.7542
0.6874
RelGT
(0.0340)
(0.0384)
(0.0641)
(0.0339)
(0.0189)
(0.0126)
(0.0682)
(0.0239)
(0.0836)
0.8264
0.7158
0.7364
0.8096
0.8791
0.8777
0.8602
0.7614
0.7413
rel-HNN-oue
(0.0776)
(0.0180)
(0.0582)
(0.0642)
(0.0103)
(0.0496)
(0.0193)
(0.0182)
(0.0131)
0.6515
0. 7.7997
0.7041
0.8166
0.8728
0.8976
0.8553
0.7563
0.7363
rel-HNNNav
(0.1485)
(0.0205)
(0.0336)
(0.0641)
(0.0160)
(0.0560)
(0.0118)
(0.0143)
(0.0283)
0.8916
0.7023
0.6521
0,8250
0.8814
0.8697
0.8685
7.76
0.7712
rel-HNNNono-t
(0.0596)
(0.0111)
(0.0825)
(0.0646)
(0.0105)
(0.0537)
(0.0263)
(0.0232)
(0.0117)
0.8667
0.6982
0.5119
0.8218
0.8732
0,7300
0.7541
0. 0.7206
0.7418
rel-HNNNav-t
(0.0287)
(0.0260)
(0.0239)
(0.0647)
(0.0238)
(0.2495)
(0.1261)
(0.0488)
(0.0718)
Among the rel-HNN variants, rel-HNNN-neet (rel-HNNN with one-hot encoding and table embedding) achieves the highest AUROC scores on most datasets, including Hepa (0.8916), SomeGen (0.8250), IMDB (0.8814), rel-ft (top3) (0.8685), rel-ft (dnf) (0.7616), and rel-avito (ux) (0.77.2). Rel-HNN-ono-t demonstrates com- petitive AUROC performance on the remaining datasets as well. The standard deviations for this variant are also relatively low or comparable across datasets, indicating stable and consistent performance. Among the other three versions of rel-HNN, rel-HNN-av-- is a strong contender that outperforms the other two variants on datasets such as Hepo, SameGen, IMDB, and rel-avitoouv). The benefit of lcarning table embed- dings explicilly is evident. from the performance gains of the -I variants. Both rel-HNNN-oee and rel-HNN-av exhibit notable improvements after the concatenation of the table embedding that represents the global in- formation of all tuples in a table. An important observation is that in the datascts where the non-t variants perform relatively better such as, the number of tables is relatively small, suggesting that explicit table-level embeddings may be less beneficial when the relational structure is simple or shallow. For example, there
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are only three tables in both Cora and Mutag, where the non-t variants perform better. For the dataset Pima, the number of tables is relatively higher (nine), but the performance gap in favor of non-t variants is also insignificant compared to Cora and Mutag. However, compared to existing state-of-the-art methods, rel-HNN-one and rel-HNN-av demonstrate substantial performance gains, despite not utilizing table-level embeddings.
The performance results presented in Table when analyzed alongside the dataset statistics in Table 5 reveal several key tccoos.Across diverse range of datasets, spanning small-scale ones such as Pima and SameGen, medium-scale datasets like Hepa, Cora, and Mutag, and large-scale datasets including rel-fl (top3), rel-fl (dnf), and rel-avito (we), our proposed methods consistently achieve superior performance. The relative performance gain of our models is substantially higher on smaller datasets such as Hepo (34.2%), Pima (17.9%), Cora (13.1%), and SomeGen (27.6%), with Mutag (1.9%) being a notable exception. In contrast, the improvement is more moderate for larger datasets, including IMDB (2.78%), rel-ft (top3) (5.5%), and rel-ft (daf) (1.0%), while rel-avito (un) (12.2%) the largest datasct, deviates from this trend. This highlights a key limitation of existing state-of-Che-art methods in coping with both extremes of data availability, struggling to generalize in data-scarce (small-scale) settings and to scale efficiently in large-scale environments. In summary, our methods adapt effectively to varying levels of schema complexity and data volume, making them suitable for both compact and large-scale relational databases. Rel-HNN-nne-t emerges as the most robust model, delivering both high AUROC scores and low variance. These results collectively demonstrate the effectiveness of our proposed framework on classification problem across diverse relational datasets.
5.3 Performance on Regression Tasks
For regression tasks, we have collected five relational datascts (sce Appendix for detailed datasct descrip- tions). Table 2 reports the Root Mean Square Error (RMSE) performance across four regression dalasets for various methods. Across all datasets, the proposed rel-HNN variants significantly outperform the state-of- the-art methods in terms of RMSE values. On the Pyrimidine dataset, which is relatively small, rel-HNN-onne achieves the lowest RMSE of 0.0792, outperforming SPARE-GCN, the best-performing baseline method, by a substantial margin. Other variants of rel-HNN have also performed better than the existing approaches. In the ClassicMoodel dataset, which contains more tables and a larger schema,, the RelGT performs the best among existing methods with an RMSE value of 537.7372. In contrast, the rel-HNN variants reduce the error significantly, with rel- rel-HNN-av-t achieving an RMSE value of 115.8039. This demonstrates the better generalization capability of our models in complex relational structures. The improvements are even more pronounced in the Pubs dataset. Here, RelGT yields an RMSE of 72.4847, while all rel-HNN versions reduce the error drastically to around 6, with vel-HNN-one achieving the lowest RMSE of 5.4055. This suggests that the rel-HNN architecture is particularly effective in datasets with rich attribute columns and intri- cate relational dependencies. For the Biodegradability datasct, ReIGT again achieves the best performance among prior approaches, with an RMSE of 18.4748. In contrast, -oo-e-- significantly outperforms all baselines, achieving the lowest RMSE of 1.4779. Other rel-HNN variants also attain RMSE values around 1.5, demonstrating the scalability and effectiveness of our method in handling large and complex relational datasets. For the rel-fl (position) dataset, RelGT substantially reduces the RMSE to 6.7498 compared to prior baselines, while rel-HNN-one-t attains the lowest RMSE of 3.4585.
Overall, the rel-HNN models consistently achieve lower RMSE values and greater stability across datasets, as indicated by smaller standard deviations in most cases. The different variants of rel-HNN demonstrate unique strengths. Rel-HNN-one achieves the lowest RMSE score on Pyrimidine, which consists of two tables only, suggesting its effectiveness for limited schema complexity. On the other hand, for larger and complex datasets with a higher number of tables, the table embedding variants (one-t and av-t) tend to perform better. Rel-HNN-oneet provides the best performance on Pubs (5.4055), rel-fl (position (3.4585), and Biodegradabilityy (1.4779), and near-best performance on Classic.Mdels (117.5294). Rel-HNN-av-t achieves the best performance on Clussic.Moeess (115.8039) and is narrowly out performed on Pubs (5.6332) by rel- HNN-one-t. For the ClassicModels dataset, although rel-H-N-av-t is outperformed by both rel-HNN-one and rel-HNN-ono-t, performs better than its non-table counterpart, rel-HNN-av. Overall, the ficxibility among variants allows the rel-HNN framework to adapt effectively across a broad spectrum of relational learning tasks.
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Table 2: RMSE Comparison Across Datasets in Regression Tasks
Method
Pyrimidine
ClassicModcls
Pubs
Biodogradability
rel-fi (position)
GCN
0.1195 1 0.0591
955.4509 1 80.8273
225.6450 1 20.0912
18.8374 1 5.3645
15.1948 1 2.7599
GAT
0.1183 + 0.0530
989.2736 + 80.5091
231.1827 + 16.7465
19.5109 + 5.1827
15.6837 + 2.4883
SPARE-GON
0.1061 + 0.0517
809.0918 + 67.7364
125.6509 = 12.9283
14.3746 = 7.6509
16.3938 = -2.3948
SPARE-GAT
0.1149 + 0.0469
808.6547 + 59.9182
142.8374 + 15.6501
18.0928 + 4.9821
15.3443 + 3.1913
HyperGCN
0.1283 + 0.1329
921.5709 102. 4283
130.8904 = 18.3423
18.4759
7.1238
15.4839 3.0293
HGNN
0.1212 1: 0.1385
914.5710 1: 98.8957
4856 1 17.6765
18.1492
6.7865
15.3847 1 2.9749
DPHGNN
0.1035 + 0.0469
821.7203 = 73.3498
107.2941 = 12.9347
19.8453 = 6.8463
14.3847 = 2.4759
ATJ-uct
0.1235 1 0.0331
728.8391 1. 52.2696
95.3952 1 7.1283
24.7605
1.1405
11.2039 1 2.3948
ReIGT
0.1098 + 0.2348
537.7372 + 21.4374
72.4847 = 8.2344
18.4748 = 9.6134
6.7498 + 1.9283
rel-HNN-one
0.0792 1 0.0394
120.7200 +: 18.9351
6.0572 + 1.0807
1,5900 + 0.2871
3,7384 1 0.3095
Hel-HNNNav
0.0843 + 0.0308
117.6724 + 17.4639
6.0106 + 1.1637
1.6729 + 0.0198
3.8938 + 0.3293
-el-H
0.0975 + 0.0365
117.5294
9.2348
5.4055
1.4554
1.4779
0.4093
3.4585 + 0.2003
rel-HNN-av-t
0.0979 + 0.0574
115.8039 + 14.1657
5.6332 1.5954
0.3934 1.6390
3.6738 + 0.2039
5.4 Split-Parallel Hypergraph Learning Performance
To observe the effectiveness of parallel processing, we conducted experiments using the split learning process classification task. All experiments were performed using the rel-HNN-one-t version of the rel-HNN model. We evaluated performance on two different sets of datasets. The first set consists of the same datasets used in Section 5.2 The second set includes widely used benchmark hypergraph datasets (see Appendix E for detailed dataset descriptions). Figure presents the training time per epoch (in milliseconds) across multiple datasets under varying numbers of GPUs (N), reflecting the impact of split-learning and parallelism.
Hepa
Pima
Cora
SameGen
IMDB
Mutag
22
60
25
15
15
1,000
12
20
111
111
40
1116
200
10
10
500
18
15
20
5
N
2
3
4
N
1
2
3
4
N
1
2
3
4
N
1
2
3
4
N
2
3
4
N
1
2
3
4
rel-fl (top3)
rel-fl (dnf)
rel-avito (uv)
DBLP
Pubmed
Citescer
600
600
1,500
100
20
15
mi
4400
400
1,000
80
60
E 16
S:
18
10
200
200
111
500
20
40
12
14
5
N
1
2
3
4
N
1
3
N
1
3
N
2
3
N
2
4
N
1
2
4
Figure 4: Training time per epoch (in milliseconds) across databases for different numbers of GPUs (N)
A clear trend is observed for larger datasets in terms of number of nodes as Hepa, Com, IMDB, Mulag, rel-fl (top3), rel-f1 (dnf), and rel-avito (uv). As we increase the number of GPUs, the training time per epoch decreases substantially. For example, on the rel-f1 (dnf) dataset, the training time per epoch drops significantly from 672.33 ms with a single GPU to 211.42 ms with four GPUs- a speedup of nearly 3.18x. This indicates the substantial benefit. of parallel training for large datasets. The speedup tends to diminish as the dataset size decreases, primarily due to insufficient workload to fully utilize multiple GPUs, leading to commmiccttin overheads diminishing the benefits of parallelization. For example, dataset Mutag, which is relatively smaller than rdd-f, the speedup drops to 1.41x, with the training time per epoch decrcasing from 23.60 ms to 16.73 ms. On much smaller datascts, such as Pima and SameGen, we observe that increasing the number of GPUs increases the training time per epoch, as we employ two GPUs. As the number of GPUs increases, the training time per epoch decreases, but fails to improve beyond the single- GPU performance. Here, the inter-GPU communication and processing overheads for aggregating hyperedge embeddings are outweighing the benefits of parallelization. Among hypergraph datasets, the most significant runtime reduction is observed the largest dataset, DBL.P, where training time drops from 98.51 ms with
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single GPU to 33.53 ms with four GPUs, achieving a speedup of nearly 2.94x. A similar decreasing trend is seen in Pubredd, which shows consistent improvement as N increases, reducing training time from 20.06 iss to 15.48 ms with a speedup of 1.30x. However, in the case of Citeseer with relatively fewer nodes, an increase in N results in a rise training time, likely due to parallelizatio overhead surpassing the computational benefits for smaller datasets. These lindings highlight that while multi-GPU split-learning effectively reduces training time for large-scale datasets, may introduce diminishing or even negative returns for smaller hypergraphs with limited computational load. A detailed theoretical analysis of the scalability characteristics of the split-paralle rel-HNN is provided in Appendix G Additional experimental results synthetic datasets are provided in Appendix F
6 Conclusions and Future Work
In this paper, we presented rel-HNN, a novel hypergraph neural network framework for learning on relational databases. By representing attribute-value pairs as nodes and tuples as hyperedges, our model captures intricate, fine-grained relationships within and across tuples without relying on schema-specific constraints like primary key-foreigh foreign key (PK-FK) relationships. Rel-HNN introduces a multi-level embedding strategy to learn reprosentations at the attribute, tuple, and table levels, offering comprchensive and expressive approach to relational data modeling. To address the scalability challenge posed by large hypergraphs, we further proposed a split-parallet learning algorithm that effectively distributes the workload across multiple GPUs. Our empirical evaluation shows that rel-HNN consistently outperforms state-of-tho-art methods in predictive performance and offers significant computational speedups through parallel training. Building on this foundation, lederated hypergraph learning and learning on relational data cloud environments can be promising directions for future research.
Acknowledgment
This research was funded by grant from the Bangladesh Bureau of Educational Information and Statistics (BANBEIS) under Project ID IC20232933. The authors gratefully acknowledge this support.
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A Relational Database and Graph Terminology Mapping
To facilitate the transformation of relational databases into graph hypergraph structures, summarize the correspondence used in the literature between relational concepts and their graph counterparts in Table 3 This mapping highlights how relational tuples, attributes, and keys are interpreted as nodes, features, and edges, respectively. Such a representation serves as the foundation for applying graph learning methods to relational data.
Table 3: Relational Database and Graph Terminology Mapping
Relational Database
Graph Representation
Row/ Tuple
Node
Table
Node type
Foreign key column
Edge type
Non-foreign-koy column
Node features
Foreign key from TA to T Label of target tuple t E T
Edge from node to node
Label of node for t of type tg
B
Notations
In Table we summarize the notations for node, hyperedge, and table embeddings, as well as the linear and non-lincar components of the MLPs employed in both node and hyperedge update steps.
Table 4: Notations for embeddings and transformation functions in rel-HNN.
Symbol
Description
X
Input feature vector of node v V E'beeddin of node at layer Intermediate embedding of hyperedge at layer Local partial hyperedge embedding of e computed on GPU Final hyperedge embedding of e at layer after non-linear activation Learnable embedding of table T
p()
ZT
MLP
Multilayer perceptron applied to nodes at layer
MLP
Multilayer perceptron applied hyperedges at layer 7
MLP
Linear component of the hyperedge MLP
MLP
Non-linear component of the hyperedge MLP applied after aggregation
C Detailed Algorithms
Algorithm [oorrithmmmiilsstttte the procedure for transforming a relational database into a hypergraph representation. Each unique attribute-value pair is mapped node, and hyperedges are formed capture the relationships across tuples within tables. The resulting hypergraph (V. E, X) serves the foundation for the proposed rel-HNN model.
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Algorithm 1: Hypergraph Generation
Input RDB: A relational database
Output: H1 X)); A hypergraph begin
V, node_eemmi _ma < 0, 0, 00:0: foreach table T E RDB do
foreach row i in T do
foreach column j in T do if T node_may map then new [ood[]
8
node tatte_map[Atttrr Tiy]l + ;
9
V vu
foreach table T E R do
foreach row i in T do e+0;
11
12
foreach column j in T do node m Tht
13
15
e e E Eu
16
17
foreach E V do
18
[ + Feature vector of node e E V
19
return
Algorithm 2: rel-HNN
Input : EV, A hypergraph, RDB: A relational database, epochs: Number of epochs
Output: ( U MLPP):: The MLP parameters for nodes and hyperedges, UTERDB Zr: The table embeddings
begin
Initialize parameters o u MLPP) and UTERDa Zr
for epoch to epochs do for E V do
2 + (X.);
for E E do
F + MLP (E Z");
7
PU 2T.); Z < CONCAT(F)
8
for 1 + + L do
10
for V V do
Z + MLP1 (Eees, 2:-1));
11
12
for e E F do
z - MLP (Euéc Z.));
13
14
L- loss flnoss_fuctttooUeee ! RDB):
Update the parameters of U MLP, U MLP and UTERdR Zr to minimize L;
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Algorithm 3: Split-Parallei rel-HNN
Input : H, = E,, X,): A hypergraph, RDB: relational database, epochs: Number of epochs
Output: O U UMLPE UMLPF, J: The MLP parameters for nodes and hyperedges, UTERDS Zr: The table embeddings
begin
Initialize parameters O u UMLPE Bc UMLPE } and UTERDB Zr
for epoch +- 1 to epochs do for u E V/; do - + (MLP((X.))
5
6
for e E E, do
7
F2. + M-MP Send Fo to U j=l GPUs;
Fi (Evee Zo):
8
Wait for User, Uz=1iز Fo, to arrive; for e E E, do
10
F + MLPE, E. (Ei=o (2) Z < CONCAT(FE e, Zr.); for I + + do do
11
12
13
14
for u E Vi do Z + MLPY (Eees, Z:-1);
15
16
for e E F. do FL. + ML (Ergr (.))
17
Send FL to UN = FPU;
Wait for USER U 1=1,1 Fi. to arrive; for E E, do
19
20
2 - MLPE, (Eno F%,
21
22
+ function(VeEE2 RDB);
Update the parameters of U MLP (LLLMMPEE M.MLEE,, and UTERDE Zr to minimize L;
23
Algorithm presents the pscudocode for the rel-HNN algorithm described in Scction The algorithm takes the hypergraph H = (V, E, X), the relational database RDB, and the epoch number as input and provides model parameters after training output. The algorithm first initializes the learnable parameters U (MLP, U MLP both for the nodes and the hyperedges, respectively. In addition, for cach table T RDB, we initialize an embedding, Zr, which are learnable parameters of the model (Algorithm 2 Line 2). After determining the class probabilities for the hyperedges corresponding to the tuples in the target table as discussed (Algorithm the loss determined previously (Algorithm Line 15).
2 Lines 4-13), the parameters are updated using backpropagation based on
Algorithm outlines the split-parallel training strategy for rel-HNN described in Section 4.3 Fach GPU processes shard of the hypergraph and computes local node and edge embeddings. Intermediate edge messages are exchanged across GPUs at every layer, ensuring that dependencies spanning multiple partitions are captured. Node and edge embeddings are then updated iteratively for L layers, and model parameters are optimized by minimizing the loss over the final edge embeddings. This strategy enables efficient multi-GPU training while preserving the integrity of relational dependencies.
D Justification of the MLP Decomposition
Proposition. Let the hyperedge MLP at layer be defined as
MLP a =
b1).... bc),
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where each We and be denote the weight and bias of layer e, and oε(-) introduces nonlinearity. We decompose MT.P into two sequential parts:
MLP Ee. (z) = Wi MLP (z) = oL(WWoL-1( (W b2)...) bL).
Claim. If the first layer is linear and subsequent transformations depend only the aggregated pre- activation, then computing
1 Σ MLP Ee (x:) and
-MLP E. (u)
is algebraically equivalent to evaluating the full MLP on the aggregated input æ = Σn
Proof. Because W1 is linear,
Σ MLP Ec (z:) Σ i=1
WIx, = Wi
Σ
Wix.
Adding the bias b once after aggregation yields the same pre-activation Wix+ bj as the original network. Since all subsequent layers of MLP E, are applicd only to this aggrogated activation, their outputs are identical to those ML.P Hence,
MLP Σ E,
MLP Eg (2i) -LP Σ x1).
which proves equivalence.
E
Datasets
For the classification task, nine different datasets are selected from various domains: Hepatitis B disease (Hepa), Diabetes discase (Pima), citation networks (Cora), Kinship information (SameGen), Movics infor- mation (IMDB), Mutagenicity information (Mutag), Formula 1 dataset (rel-fl & rel-fl(dnf)), and Online Advertisement (rel-avito(uv)). The first six datasets are from the CTU Relational Learning Reposi- tory Motl& & Schulte (2024). and the last three datasets are from The Relational Deep Learning Benchmark Repository Robiinson et al. (2024). These databases are widely used for evaluating supervised learning mod- els on classílication tasks involving relational data, where categorical altribule is to be predicted. The datasets are sclected to reflect wide range of scales, schema complexities, and classification challenges. The datasets vary from to 10 relational tables and contain up to 70 columns. All datasets are designed for binary classification tasks, except for the Cora dataset, which contains 7 distinct classes. Smaller datasets such as SameGen and Pima provide settings with limited nodes and hyperedges, enabling evaluation of model behavior in low-data. Hepa and MUTAG correspond to moderate-sized datasets with richer relational utructures. In contrast, IMDB and rel-avito (uv) represent large-scale real-world datasets with millions of nodes and hyperedges, posing significant challenges terms of scalability, memory efficiency, and long-range dependency modeling. The rel-fl (top3) and rel-fl (dnf) datascts incorporate complex relational schemas with len tables and high-dimensional feature spaces, enabling rigorous evaluation of model performance on fine-graineds real-world classification tasks under realistic relational settings. The statistics for the datasets used in classification experiments are presented in Table 5
Table 5: Statistics of Classification Dataset
rel-fl
rel-fi
rel-avito
Statistic
Hepa
Pima
Cora
SameGen
IMDB
Mutag
(top3)
(dnf)
(uv)
# of Nodes
6488
1773
7927
141
4025855
13492
85263
85607
3210239
# of Hyperedges
12927
6912
57353
1536
5793251
10324
76730
86742
107
Total # of Tables
9
3
3
10
10
8
Total of Rows
12927
6912
57353
1536
5793251
10324
76730
86742
20679117
Total # of Columns
26
18
6
8
21
14
70
70
43
# of Classes
2
2
7
2
2
2
2
2
2
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Table 6: Statistics of Regression Datasets
Statistic
Pyrimidine
ClassicModels
Pubs
Biodegradability rel-fl (position)
# of Nodes
130
5215
681
15435
85449
# of Hyperedges
296
3864
245
21895
82775
Total # of Tables
2
8
10
10
Total # of Rows
296
3864
245
21895
82775
Total # of Columns
13
59
61
14
70
Table 6 presents the statistical summary of four relational datasets used for regression tasks in Section 5.3
The first. four datasets are from the CTU Relational Learning Repository Mottl & Schulte (2024), and the dataset is from The Relational Deep Learning Benchmark Repository Robinson et al. (2024). ) The datasets exhibit significant variation in relational complexity and scale, enabling a comprehensive evaluation of model robustness and generalization. For instance, Biodegradabillly is the largest. in terms of both nodes (15,435) and hyperedges (21,895), suggesting rich relational structure and potentially complex learning dynamics. In contrast, Pyrimidine is the smallest, with only 130 nodes and 296 hyperedges, allowing us to assess performance in low-data regimes. The ClassicModels dataset shows high number of tables (8), columns (59), and classes (273), indicating a detailed schema and fine-grained prediction task. Similarly, the Puhs dataset, despite having a relatively small number of rows (245), has a large number of columns (61), which pose challenges related to feature sparsity or redundancy. Finally, the rel-fll (position) dataset represents a large-scale real-world regression setting with 85,449 nodes, 82,775 hyperedges, and 10 interconnected tables, enabling evaluation of model scalability and its ability to capture complex, long-range relations.
Table 7 presents the structural statistics of the datasets: Cileseer, DBLP, and Pubmed. Among them, DBLP is the largest in terms of both the number of nodes (41,302) and hyperedges (22,363), indicating a highly complex and densely connected hypergraph structure. Pubmed represents a medium-sized dataset with 19,717 nodes and 7,963 hyperedges, whereas Citeseer the smallest, comprising 3,312 nodes and 1,079 hyperedges. Despite its relatively small scale, Citeseer has the highest feature dimensionalitty, with each node represented by 3, d..0nniimensional feature vector. In contrast, Pubmed and DBLP exhibit more moderate feature lengths of 500 and 1,425, respectively. The diversity across these datasets in terms of size and feature complexity makes them well-suited for evaluating the effectiveness of the proposed split-parallel hypergraph learning approach.
Table 7: Statistics of Benchmark Hypergraph Datasets
Property
DBLP
Pubmed
Citeseer
Number of Nodes
41,302
19,717
3,312
Number of Hyperedges
22,363
7,963
1,079
Length of Feature Vector
1,425
500
3,703
F
Hypergraph Learning Performance on Synthetic Datasets
In addition to real-life benchmark hypergraphs (Section 5.4 1 we have also experimented with synthetic hypergraphs generated by varying the number nodes and hyperedges. For each hyperedge, nodes are drawn from a uniform distribution, where the number of nodes varies from three to ten. The feature vectors and labels are also randomly assigned to each node, where the feature vector length is set to 1024. We varied the number of nodes, [V], between 5,000 and 10,000, and the number of hyperedges, E., between 10,000 and 100,000 to assess performance across different hypergraph scales.
In Figure 5
we present the per-epoch training time across the synthetic hypergraphs for different numbers of GPUs As the size of the hypergraphs increases-eitee in terms the number of nodes or hyperedges the advantages of parallel learning become more pronounced. For the smallest hypergraph with =|05,000 and 10,00000 increasing the number of GPUs leads to an increase in training time, due to synchronization overheads. However, as the number of hyperedges is increased to 50,000 and 100,000, the am achieves
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5
V -V
|V
[ -V=k
10k
=0k=50k
[ = 100k
[ = 10k
E =5k
E 100k
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30
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40
E
100
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3
4
N
1
2
3
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N
1
2
3
4
N
2
3
4
N
1
2
3
4
Figure 5: Training time per epoch (in milliseconds) across synthetic hypergraphs for different numbers of GPUs (N)
an increasing speedup of 1.98x and 2.33x, respectively. For the hypergraph with [V] = 10,000 and [F] = 10,000, the training time decreases from 16.19 ms (with single GPU) 13.84 ms when using two GPUs. Interestingly, more GPUs are employed, the training time begins to increase, reaching 14.22 ms with 1.13x speedup for four GPUs, indicating underutilization of parallel resources. Again, increasing the number of hyperedges to 50,000 and 100,000 results in higher speedups of 2.69x and 3.02x, respectively. A similar trend is observed with an increasing number of nodes. For instance, with E = 100,000, increasing the number of nodes from 5,000 to 10,000 enhances the speedup from 2.33x to 3.02 These results demonstrate that while synchronization overheads can limit speedup gains on smaller hypergraphs, the proposed parallel learning approach achieves substantial performance improvements as the size and complexity of the hypergraphs scale.
G Scalability Analysis of Split-Paralle, Hypergraph Learning
For rel-HNN algorithm, the time complexity of computing the initial node embedding 2 for a node V, obtained by applying MLP to its input feature vector X, (Algorithm 2 line 5), is (nfdd), where n = |V] is the number of nodes, f is the input feature dimension, and d is the embedding dimension. For E, the computation of the intermediate hyperedge embedding F!!)) the initial layer (Algorithm 2 line 7) has time complexity of O(end+ where e = |P| denotes the number of hyperedges. At each subsequent layers, the complexity of updating the node embedding z!O) (Algorithng (ned +ndd,, while determining the hyperedge embedding z requires O edd). The total complexity of single propagation can be expressed as
each hyperedge
2 line 11) is
(LL(ndd + end + edd)).
For the
rel-HNN executed on two GPUs, the overall time complexity can be expressed as OL (Endd + lend + edd + T(ed))
where and denote the numbers of nodes and hyperedges, respectively, and L is the number of layers. Since the node set is evenly partitioned across two GPUs, the local computation cost for node and hyperedge updates is effectively halved. The term T(ed) represents the communication overhead incurred during inter-GPU synchronization, specifically for exchanging and aggregating local hyperedge embeddings via the AllReduce operation. This communication cost can be modeled as T(ed) = A + Ced, where A denotes the latency or startup cost associated with initiating the communication, and is the per-hyperedge- dimension transfer and aggregation cost. that depends on the interconnect bandwidth and the efficiency of the collective communication backend. The split-parallel execution yields performance advantage when the computational cost of the singlc-GPU setting exceeds that of the distributed formulation, i.c.,
L(ndd + end + edd) > L nddd + eend + edd + T(ed)),
where the first term represents the total computation cost on single GPU and the second term includes the reduced per-GPU computation together with the inter-GPU communication overhead T(ed). This condition
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Rearranging terms, we obtain which can be further simplified to Published in Transactions on Machine Learning Research (12/2025)
can be simplified as Substituting the communication model T(ed) = + Ced into the inequality yields
ndd + end > 2ndd + gend + T(ed).
2 ndd + } end > + Ced.
2
ndd ed (
C) > A,
e c - C)
>
4 nd d
2
(9)
When the number of nodes n is sufficiently large compared to the communication constants A and C, we have 2 C' O. Using Equation g the inequality can be rearranged as
A
>
d
(10)
C
As n becomes sufficiently larger than the communication constants and C, the right-hand side of Equa- tion I0 becomes negative. Since the number of hyperedges always positive, the inequality is naturally satisfied. Therefore, the split-parallel rel-HNN yields better performance when the number of nodes is significantly large, the computational gain from parallelization outweighs the communication overhead. Note that, in environments with lower communication overhcads (smaller values of A and C), the minimum number of nodes for speedup correspondingly decreases.
When the number nodes n is relativoly small compared to the commuication constants A and C, the term p - C becomes negative. In this case, Equation can be rearranged as
A
nd 7 2
(11)
C
As the right-hand side of Equation 1I is negative, satisfying the inequality requires the number of hyperedges e to be negative, which is infeasible. This condition indicates that, for smaller graphs or limited node counts, the communication overhead dominates the computation cost, and the split-parallel execution provides no performance gain. In other words, when n is relatively small, the latency and bandwidth terms (A and C)) outweigh the computational savings from parallelization, making the single-GPU configuration more efficient. The analytical findings are supported by the experimental results. As shown in Figure 4 for the datascts Pima, SumeGen, and Cileseer, the single-GPU execution outperforms the split-parallel configuration. From Tables 5 and 7, it can be observed that all these datasets contain relatively few nodes (less than 5,000), which aligns with the analytical conclusion that split-parallel exccution is less effective when the number of nodes is small.
Hyperparameter Sensitivity Analysis
To analyze the sensitivity of our model to key hyperparameters, we conducted experiments to evaluate its performance under different parameter settings. Figure presents the AUROC scores of rel-HNN for varying numbers of layers (L). In most cases, the best performance is achieved with shallow architecture (L=2) while deeper configurations (L 4) lead to gradual degradation in accuracy. This trend indicates that rel- HNN effectively captures essential higher-order relationships within only a few message-passingg steps, and increasing the depth beyond that introduces redundancy and over-smoothing. For smaller and moderately sized datasets (e.g., Hepa, Pima, SameGen, Mulag, and Cora), additional layers tend to diffuse information excessively, causing embeddings of connected nodes and hyperedges to become indistinguishable, a well- known phenomenon in graph learning models. Larger datasots, such as IMDB, (top3), rel-f1 and rel-avito(wu) uu), exhibit a similar but less pronounced decline, suggesting that even complex relational structures, shallow architectures sufficient to achieve optimal relational aggregation. Overall, these results
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demonstrate that rel-HNN attains peak performance with two layers, striking a balance between expressive power and generalization while avoiding over-smoothing and unnecessary computation.
Hepa
Pima
Cora
SameGen
TMDB
0.9
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L
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8
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Figure 6: Performance (AUROC) variation with respect to the number of layers (L) across datasets.
Hepa
Pima
Cora
SameGen
IMDB
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Figure 7: Performance (AUROC) variation with respect to the embedding dimension (d) across datasets.
To further analyze the sensitivity of rel-HNN to key hyporparameters, evaluated its performance across different embedding dimensions (d), as shown in Figure 7 In most datasets, the highest AUROC scores are achieved with dow-dimensional embeddings (d = 2), while performance gradually decreases as the em- bedding dimension increases. This trend suggests that small embedding spaces are sufficient to capture the underlying relational structure in the data, whereas higher dimensions may introduce redundant or noisy features that hinder generalization. For smaller and moderately sized datasets (e.g., Hepu, Pima, Mulag, and SameGen), increasing the embedding dimension leads overparameterization relative to the available data, rosulting in reduced discriminative power. Even for larger relational datascts such rel-fl and rel-avito, the improvement from higherr-dinensional embeddings remains marginal, with performance saturating slightly decreasing beyond d = 4. Overall, these results indicate that compact embedding representations are sufficient for rel-HNN, providing an efficient trade-off between model complexity, generalization, and training cost.
Memory Consumption Analysis
To analyze the memory characteristics of our model, we conducted experiments using the split-parallo rel-HNN configuration. Figure X illustrates the peak GPU memory consumption of the split-parallel rel-
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Published in Transactions on Machine Learning Research (12/2025)
HNN with varying numbers of GPUs (N). Across all datasets, consistent downward trend is observed N increases, confirming the scalability of our parallelization strategy. As the number of GPUs increases from one to four, memory consumption per GPU decreases substantially - for instance, in the rel-fl (top3) dataset, peak memory usage drops from 5172 MB to 1555 MB, representing a reduction of nearly 70%. However, the decline is not strictly linear, as minor deviations are introduced by the additional communication buflers used during the AllReduce operations required for inter-GPU synchronization. The reduction rate lower for smaller datasets, such as SomeGen and Pima. Overall, the results validate that the split-parallel rel- HNN achieves effective memory scaling across multiple GPUs, enabling training on larger datasets and higher-dimensional embeddings.
Hepa
Pima
Cora
SameGen
IMDB
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Mutag
rel-f1 (Lop3)
rel-f1 (dnf)
rel-avito (uv)
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Figure 8: Peak Per memory consumption (MB) with varying numbers of GPUs (N) across datasets.
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Reviews:
[{"review_id": "hYgiJaHubN", "reviewer": "Reviewer_dcwG", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces Rel-HNN, a novel hypergraph-based framework for learning on relational databases. The core idea is to model unique attribute-value pairs as nodes and database rows (tuples) as hyperedges. This schema-agnostic approach effectively captures fine-grained intra-tuple relationships without relying on predefined primary/foreign key constraints. To address scalability, the authors also propose a split-parallel training algorithm that distributes the computation across multiple GPUs.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Performance Superiority: The central claim that Rel-HNN outperforms state-of-the-art methods is strongly backed by extensive experimental results presented in Tables 1 and 2. The performance margins on both classification and regression tasks are significant and consistent across a wide variety of datasets.\n\nNovelty of Representation: The proposed hypergraph representation is clearly explained and illustrated in Section 4.1 and Figure 1, substantiating the claim of a novel approach to modeling relational data.\n\nScalability and Speedup: The claim of computational speedup via the split-parallel algorithm is convincingly demonstrated for large-scale datasets in Figure 4. The evidence is also transparent in showing the limitations on smaller datasets where overhead can diminish gains, which adds to the credibility of the evaluation.", "audience_yn": "Yes", "audience_explanation": "Researchers in Graph Neural Networks (GNNs) and Hypergraph Learning: The paper introduces a novel and effective application of hypergraph neural networks, demonstrating their power on a challenging, structured data domain.\n\nThe Machine Learning Systems Community: The split-parallel training algorithm for scaling hypergraph learning across multiple GPUs is a direct contribution to research on scalable and distributed ML.\n\nPractitioners and Researchers Working with Structured Data: Applying deep learning directly to relational databases without costly and lossy \"flattening\" is a major practical problem. This paper offers a high-performing solution that would be widely appealing.", "requested_changes": "1.  Clarify the Formulation for Parallel Learning: Refine the mathematical exposition in Section 4.3. Specifically, provide a clear justification for the MLP decomposition (into linear and non-linear parts), unify the notation for local and global embeddings (e.g., G, F, Z), and specify the mechanism for inter-GPU message passing.\n\n2.  Contextualize the HNN Architecture's Novelty: In Section 4.2, please add a brief comparison of the rel-HNN message-passing scheme to a relevant prior hypergraph network (e.g., AllSet). This will help distinguish the architectural innovations from the highly novel data representation.\n\n3.  Provide Deeper Analysis of Scalability: Expand the discussion in Section 5.4 to include a more rigorous analysis of the trade-off between parallelization benefits and communication overheads, especially for smaller datasets. Offering practical guidance or a heuristic for when to use the split-parallel approach would significantly increase the paper's impact.", "broader_impact_concerns": "No", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "GomP553Kks", "reviewer": "Reviewer_DbVS", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes  a hypergraph neural network framework for learning directly on relational databases(rel-HNN). Instead of flattening data or modeling each tuple as a single node , this model represents each attribute–value pair as a node and each tuple as a hyperedge, thereby capturing fine-grained intra-tuple and inter-tuple relationships.\nThe model learns multi-level embeddings at the attribute, tuple, and table levels, and introduces a split-parallel training strategy that partitions computation across multiple GPUs to address scalability challenges. Some results across diverse classification and regression benchmarks show consistent improvements over state-of-the-art baselines, alongside significant training speedups. \n\nPros:\n- This is an interesting problem; as data are increasingly stored in databases, a method of this kind could substantially improve the applicability of GNNs in industrial settings.\n- The rel-HNN’s embedding design is impressive, as it offers a clear path toward capturing both localized and global relational patterns, thereby enabling richer relational learning\n- The experimental results are promising, particularly with respect to the training speedups achieved\n\nCons:\n- Some related work seems to be missing from the discussion, such as recent advances in “Graph Neural Networks for Tabular Data Learning” (CT Li et al., 2024) and hypergraph modeling approaches for databases highlighted in “Graph Neural Networks for Databases: A Survey (Z Li et al.)”. These should be discussed in the paper to provide a more complete context.\n- The organization of the paper could be improved; for example, the definition of hypergraphs in Section 3.1 might be more appropriately placed in Section 2？\n- Some technical choices are not clearly justified; for instance, it is unclear why Rel-HNN adopts a two-phase message-passing mechanism rather than exploring more phases？", "claims_and_evidence_yn": "Yes", "claims_explanation": "The claims are generally supported by solid experiments and clear comparisons, though certain technical choices and related work discussions could be elaborated further to strengthen the evidence. The code seems can work.", "audience_yn": "Yes", "audience_explanation": "The paper addresses relational database learning with a hypergraph-based approach, which is of clear interest to TMLR readers in graph learning and data-driven applications", "requested_changes": "- **Related Work:** The paper would benefit from a more comprehensive discussion of related literature. In particular, recent advances in *Graph Neural Networks for Tabular Data Learning* (CT Li et al., 2024) and hypergraph modeling approaches for databases discussed in *Graph Neural Networks for Databases: A Survey* (Z Li et al.,  2025) should be included to better situate the proposed method within the existing body of work. This is critical to securing my recommendation.\n\n- **Paper Organization:** The organization of the paper could be improved. For example, the definition of hypergraphs currently presented in Section 3.1 would fit more naturally in Section 2 (Background), which would improve clarity and flow.\n\n- **Technical Justification:** Some design choices are not sufficiently explained. For instance, the rationale for adopting a two-phase message-passing mechanism in Rel-HNN is unclear—further justification for why more phases were not explored would strengthen the contribution.\n\n- **Practical Evaluation:** If the experiments included validation on a real relational database system (e.g., PostgreSQL), it would greatly enhance the usefulness of the paper, since the core motivation is to operate effectively on relational databases.", "broader_impact_concerns": "The work is primarily methodological and does not present obvious ethical risks. However, since relational databases often contain sensitive data (like medical or financial records), the authors may consider adding a brief note on potential concerns regarding privacy and fairness when deploying such models.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "upe53736P5", "reviewer": "Reviewer_qMXB", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces Rel-HNN, a hypergraph neural network approach for learning on relational databases that represents attribute-value pairs as nodes and tuples as hyperedges. The key contributions include: (1) a hypergraph representation that captures fine-grained intra-tuple relationships without relying on primary key-foreign key constraints, (2) a multi-level embedding strategy across attribute-value, tuple, and table levels, and (3) a split-parallel GPU training algorithm for scalability on large databases.", "claims_and_evidence_yn": "No", "claims_explanation": "1. The paper primarily compares against older GNN methods (GCN, GAT), SPARE, and ATJ-Net. Critical baselines [1] [2] and recent advances (e.g., DPHGNN [3]) in relational learning and hypergraph neural networks are not adequately covered. \n2. Many datasets are relatively small (e.g., Bupa with 495 nodes, SameGebn with 141 nodes), which may not fully validate scalability claims for \"large-scale\" relational databases.\n3. The paper lacks thorough ablation studies to understand which components contribute most to performance gains.\n\nReferences:  \n[1] Hypergraph Neural Networks, AAAI 2019  \n[2] HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs, NeurIPS 2019  \n[3] DPHGNN: A Dual Perspective Hypergraph Neural Networks, KDD 2024", "audience_yn": "Yes", "audience_explanation": "The work applies neural networks to relational databases. This is relevant to the TMLR audience for several reasons:\n- Relational databases are ubiquitous in real-world applications\n- The hypergraph perspective is practical for structured data representation", "requested_changes": "1. **Stronger baseline comparison**: Include classic and more recent hypergraph neural network methods (HyperGCN, HGNN, HGNN+) and more recent relational learning approaches as listed in references.\n2. **Critical re-evaluation of split-parallel training claims**: The paper claims \"substantial speedups\" but Figure 4 reveals that split-parallel training only provides benefits on the largest datasets (rel-f1 datasets and DBLP), while actually degrading performance on smaller datasets due to communication overhead. This limits the practical applicability of the approach. There are some suggestions:\n   - Clearly specify the dataset size thresholds where split-parallel training becomes beneficial\n   - Provide theoretical or empirical guidelines for when to use single-GPU vs multi-GPU training\n3. **Better scalability validation**: Test on truly large-scale databases (millions of tuples) to validate scalability claims. Current largest dataset has ~87K tuples.\n4. **Ablation studies**: Systematically evaluate the contribution of table embeddings, different node feature encodings, and the multi-level architecture.\n5. **Hyperparameter sensitivity**: Analyze sensitivity to key hyperparameters like number of layers, embedding dimensions.\n6. **Memory analysis**: Provide detailed memory consumption analysis for the split-parallel approach.", "broader_impact_concerns": "The paper does not require a broader impact statement. The work focuses on improving machine learning techniques for database analysis, which has generally positive implications for data science applications. No obvious negative societal implications are apparent, though the authors could briefly discuss potential privacy considerations when analyzing sensitive relational data.", "additional_comments": "Some figures (especially Figure 3) are cluttered and hard to interpret. Algorithm descriptions could be more precise and writing need to be more precise in places.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)