PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment
Authors: Qi Yu
Zhichen Zeng
Yuchen Yan
Zhining Liu
Baoyu Jing
Ruizhong Qiu
Ariful Azad
Hanghang Tong
Venue: ICLR 2026 Poster (2026)
Keywords: Network Alignment
Graph Machine Learning
Primary area: datasets and benchmarks

Abstract: Network alignment (NA) aims to identify node correspondence across different networks and serves as a critical cornerstone behind various downstream multi-network learning tasks. Despite growing research in NA, there lacks a comprehensive library that facilitates the systematic development and benchmarking of NA methods. In this work, we introduce PLANETALIGN, a comprehensive Python library for network alignment that features a rich collection of built-in datasets, methods, and evaluation pipelines with easy-to-use APIs. Specifically, PLANETALIGN integrates 18 datasets and 14 NA methods with extensible APIs for easy use and development of NA methods. Our standardized evaluation pipeline encompasses a wide range of metrics, enabling a systematic assessment of the effectiveness, scalability, and robustness of NA methods. Through extensive comparative studies, we reveal practical insights into the strengths and limitations of existing NA methods. We hope that PLANETALIGN can foster a deeper understanding of the NA problem and facilitate the development and benchmarking of more effective, scalable, and robust methods in the future. The source code of PLANETALIGN is available at https://github.com/yq-leo/PlanetAlign

Published as a conference paper at ICLR 2026
PLANETALIGN: A COMPREHENSIVE PYTHON LIBRARY FOR BENCHMARKING NETWORK ALIGNMENT
Qi Yu', Zhichen Zeng', Yuchen Yan', Zhining Liu', Baoyu Jing', Ruizhong Qiu' Ariful Azad?, Hanghang Tong' Universiity of Illinois Champaann 2 Texas A&M University "qqiyu, zhezzenz, yucheny5, 1iu326, baoyuj2, rg5, htong) 8ill Billinois... edu 2 Pari ful atmuu. edu
ABSTRACT
Network alignment (NA) aims to identify node correspondence across different net- works and serves as a critical cornerstone behind various downstream multi-network learning tasks. Despite growing research in NA, there lacks comprehensive li- brary that facilitates the systematic development and benchmarking of NA methods. In this work, we introduce PLANETALIGN, a comprehensive Python library for network alignment that features a rich collection of built-in datasets, methods, and evaluation pipelines with easy-to-use APIs. Specifically, PLANETALICO LIGN integrates 18 datasets and 14 NA methods with extensible APIs for easy use and development of NA methods. Our standardized evaluation pipeline encompasses a wide range of metrics, enabling systematic assessment of the effectiveness, scalability, and robustness of NA methods. Through extensive comparative studies, we reveal practical insights into the strengths and limitations of existing NA methods. We hope that PLANETALIGN can foster a deeper understanding of the NA problem and facilitate the development and benchmarking of more effective, scalable, and robust methods in the future. The source code of PLANETALIGN is available at https: //t///gttttt github....... com/ Sonnnyq--eee Srgglee/PPaaetaae
INTRODUCTION
Multi-sourced and multi-layer networks are becoming ubiquitous across a wide range of domains in the era of big data and AI (Lin et al., 2024; Wei et al., 2026), ranging from social network analysis (Shao et al., 2023; Qiu al., 2023; Liu et al., 2024d; Ráez & Zhang, 2024; Peng al., 2025), anti-money laundering (Zhang et al., 2021; Bei et al., 2023), bio-informatics (Zeng et al., 2023b; Hu et al., 2024; Zare Mirak-Abad & Ghorbanali, 2025; Zeng et al., 2024b), to knowledge graph fusion (Yan et al., 2021a; Chen et al., 2024b; Liu al., 2025; Ai et al., 2025), infrastructure network modeling (Qiu et al., 2022: Wang et al., 2023; Xu et al., 2024a; Zou et al., 2025b; Lin et al., 2025b;a; He al., 2026; Lin al., 2026), and unstructured adaptation (Xu et al., 2024b; Bao et al., 2024; Liu et al., 2024c; Wu et al., 2024; Zou et al., 2025a; et al., 2025; Bao et al., 2025; Zeng et al., 2025c;d; 2026). Identifying the same node across different networks, i.c., network alignment (NA), enables joint learning across multiple networks and serves as the key cornerstone of multi-netoork tasks. For example, aligning users across online social networks improves personalized services, e.g.. cooss- recommendation (Liu et al., 2023a; Yu et al., 2025; Zeng et al., 2025a;b; Yoo al., 2025a:b.. Aligning suspicious accounts from different transaction networks facilitates the detection of fraudulent activity (Zhang et al., 2019b; Du et al., 2021; Yan et al., 2024b;a;c; Qiu et al., 2024; Chen et al., 2026). In protein interaction networks, alignment of proteins across different species uncovers hidden biological homologies (Clark & Kalita, 2014; Hu al., 2024). In knowledge graphs (KGs), merging incomplete KGs based on aligned entities helps construct unified knowledge bases (Yan al., 2021a; Liu et al., 2023b; 2024a; Chen et al., 2024b).
Despite growing interest in NA, there lacks a comprehensive benchmark to provide standardized evaluation of NA methods different datasets from various aspects. The absence of such benchmarks leaves the genuine performance and usefulness of existing NA methods an open research question, hindering the standardization of research in the NA community. Although prior efforts, which are

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summarized in Table 1, have been made in benchmarking NA methods (Clark & Kalita, 2014; Cao & Yu, 2016; Sun et al., 2020: Trung et al., 2020; Döpmann, 2013). they suffer from at least one of the following limitations: (1) limited datasets within single domain, e.g. biological networks (Clark & Kalita, 2014) or social networks (Cao & Yu, 1);6); (2) limited methods exclusively focusing on a single category, e.g., consisteney-based methods (Döpmann, 2013) or embedding-
methods (Sun et al., 2020), while ignoring the most recent line of works, c.g., optimal transport (OT) based methods; (3) limited and inconsistent evaluation from a single aspect, e.g. effectiveness (Clark & Kalita, 2014; 4; Cao & Yu, 2016), without standardized dataset splits and evaluation metrics (Clark & Kalita, 2014; Cao & Yu, 2016; Sun et al., 2020; Trung et al., 2020; Döpmann, 2013).
In response to these limitations, we introduce PLANETALIGN, an open-source Py Torch-based library designed for unified evaluation and streamlined development of NA methods, which features the following key design. Firstly, PLANETALIGG includes 18 different public datasets spanning 6 different domains which can be directly downloaded through a simple API call, including social networks (Zhang& & Philip, 2015; Zhang & Tong, 2016), publication networks (Tang et al., 2008; Yang et al., 2016; Leskovec et al., 2007), biological networks (Stark et al., 2006; De Domenico et al., 2015b; Zitnik & Leskovec, 2017: Park et al., 2010), knowledge graphs (Sun et al., 2017), infrastructure networks (Yan et al., 2022: Zhu et al., 2021; Song et al., 2020), and communication networks (Zhang al., 2017; Kunegis, 2013), covering both real-world and synthetic scenarios (Limitation #1). The wide range of datasets built into PLANETALIGN allows comprehensive evaluation of NA methods on different types of networks, c.g., plain and attributed networks. fostering in-depth understanding of the applicability of NA methods to different domains. Secondly, PLANETALIGN features efficient implementations of 14 different NA methods including consistency-based (Singh al., 2008; Zhang & Tong, 2016), embedding -bsedd (Liu et al., 2016; Heimann et al., 201 8; Chu et al., 2019; Zhang et al., 2020; 2021; Gao al., 2021; Yan et al., 2021b; Liu et al., 2023a), and OT-based methods (Zeng et al., 2023a; 2024a; Tang et al., 2023; Yu et al., 2025), covering traditional and state-of-the-ar baselines (Limitation #2). With easy-to-use APIs, PLANETALIGN allows streamlined comparison between NA methods across diverse benchmark settings. Thirdly. PLANETALIGN highlights comprehensive list of evaluation metrics and benchmarking tools (Limitation #3). For evaluation metrics, we include the most classical effectiveness metrics, Hits @ K and MRR, under different pairwise alignment settings. We also include time and memory overheads for evaluating the efficiency and scalability of NA methods. For benchmarking tools, enforce consistent dataset split through a unified API design to ensure reproducibility. PLANETALIGN also provides rich collection of APIs and utility functions that allows fair and reproducible benchmarking across key dimensions of NA performance. Finally, PLANETALIGN designs extensible APIs and implements efficient utility functions, allowing users to streamline the implementation of customized NA methods and the integration of customized datasets with minimal efforts. Specifically, our API design allows customized datasets and NA methods to be built upon carefully designed base classes and integrated into PIANETALIGN'S pipeline with only few lines of code. PLANETALIGN further provides commonly used utility functions for NA algorithms, such as random walk with restart (RWR) embedding, anchor-based embedding, etc. Empowered by the aforementioned features, PLANETALIGN addresses the limitations of existing NA benchmarks comprehensively.
Based on PLANETALIGN, scalability. robustness, and sensitivity to supervision of 14 built-in NA methods across 18 built-in datasets, revealing both theoretical and practical insights into the strength and limitations of existing NA methods. We also compare PLANETALIGGN implementation of NA algorithms with their official implementation, which shows that our implementations achieve up to 3 times speed-up while maintaining similar effectiveness performance, demonstrating the superiority of PLANETALIGN.
conduct comprehensive experiments to evaluate the effectiveness,
In summary, we introduce unified, comprehensive, and efficient library PLANETALIGN featuring wide range of built-in datasets and NA methods, as well as extensible and easy-to-use utility functions and APIs, facilitating the benchmarking and development of NA methods.
2 PROBLEM DEFINITION
An illustration of NA problems shown in Figure 1. Given two input networks gi = E.)) G2 = A2, X2, Ez} and a set of anchor node pairs L = v))l E Vi,y E V2) indicating pre-alignment, where Vi, V22 denote the node sets, A₁, Ag denote
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Sent -supervised Plain MA
Sen--supervised attributed NA
Insupervised Attributeed
B
JAA
BBB
ellp
91
9z
edges
edge attributes
Mithout
attributes
links
Figure I: An illustration of NA problems.
the graph adjacency matrices, X,, X2 denote the node attribute matrices, and E,, E2 denote edge attribute matrices, the semi-supervised atrributed network alignment task aims to discover node-level correspondence across two networks inferred from an output alignment matrix S, where S(x,y) y) indicates the likelihood of alignment between node z E Vi and node y E V2. If neither node attributes X,, X2 nor edge attributes E,, E2 are available, this becomes the semi-supervised plain network alignment task; If no anchor node pairs available, i.e., [C] this becomes the unsupervised attributed network alignment task.
3 RELATED WORK
3.1 NETWORK ALIGNMENT METHODS
Existing NA methods can be classified into three categories: consistency-bast based, embedding- bbased, and OT-based approaches (Zhang & Tong, 2020). Consistency- methods are among the earliest approaches, fornulated as optimization problems which assume structural and/or attribute consistency between node neighborhoods across networks (Singh et al., 2008; Bayati et al., 2009; Zhang & Tong, 2016; Zhang et al., 2019a). Although recent works on NA have largely moved beyond consistency principles, consistency- based methods remain important baselines for benchmarking purposes.
Embedddigg-bssee and OT-based methods represent more recent advances in the NA community, For embedding basedd methods, nodes mapped into shared low-dimensional space and aligned based on embedding similarity (Liu et al., 2016; Heimann et al., 2018; Chu et al., 2019; Zhang et al., 2020; Gao et al., 2021; Yan al., 2021b; Zhang al., 2021; Liu al., 2023a; Zheng et al., 2025). By leveraging advances in deep representation learning, embedding-basee performance and remain an active research direction. For OT-based methods, they formulate the NA problem as an optimization problem minimizing the total effort of transporting the node distribution of one graph another under set of pre-defined or learnable cost functions (Tang al., 2023; Zeng et al., 2023a; 2024a; Yu et al., 2025). Recent OT-bbased methods consistently achieve state-of-the -hetrrt performance across these three categories of NA methods, making them promising direction for future research. PLANETALIGN includes representative methods from all three kinds of methods, providing comprehensive benchmarking library.
methods have shown strong
3.2 NETWORK ALIGNMENT LIBRARIES
There are five existing benchmarks/librariess for NA, and we include a comprehensive comparison
based on the inclusion of datasets. NA methods, and evaluation dimensions, in Table 1. Specifically,
SGAPBSA (Döpmann, 2013) and CAPABN (Clark & Kalita, 2014) mainly focus on benchmarking traditional consistency-based NA methods on biological networks. ASNets (Cao & Yu, 2016) 6) benchmarks the effectiveness both consistency-bar and embedding- networks, but leaves the scalability and robustness of these methods an open research question.
methods on social
In this work, the 'network' and 'graph' used interchangeably.
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Table 1:
Comparison with existing NA benchmarks/librrris (Clark & Kalita, 2014; Cao & Yu, 2016; Sun et al., 2020; Trung et al., 2020; Döpmann, 2013). We denote whether a specific type of networks, methods, and evaluations is included in the benchmark/library.
Benchmark/LLin
SGAPBSA
CAPABN
ASNets
NAB
OpenEA
PLANETALIGN (ours)
Social
X
Communication
Publication
X
X
X
Networks
Biological
V
X
Knowledge
X
X
X
Infrastructure
X
X
X
Consistency-
-hased
/
/
X
Methods
Sinbedding-basee
X
/
/
OT-hased
X
X
Effectiveness
/
Evaluations
Scalability
X
Robustness
X
NAB (Trung et al., 2020) comprehensively evaluates the effectiveness, scalability, and robustness of both consistency-basd based and embedding srbbedding-based methods. However, NAB only includes social networks, lacking comprehensive datasets on other domains where NA also plays an important part. OpenEA (Sun et al., 2020) focuses on benchmarking ignoring networks other domains. addition, none of the existing NA libraries includes Of-based methods, which have emerged the most recent and effective line of work in the NA community.
babbedding-baseed methods on knowledge graphs.
4 DESIGN OF PLANETALIGN
In this section, we introduce the design features of PLANETALIGN, which includes comprehensive built-in datasets and NA methods (Section 4.1), unified and easy-to-use APIs (Section 4.2), as well as standardized and diverse benchmarking tools (Section 4.3).
4.1 COMPREHENSIV DATASETS AND METHODS
PLANETALIGN collects and curates 18 NA datasets across 6 different domains, covering social networks, publication networks, biological networks, knowledge graphs, infrastructure networks, and communication networks. PLANETALIGN also implements 14 existing NA methods across all categories, including consistency-basee embedding-bassee and OT-based methods. An overview of built-in datasets and NA methods in PLANETALIGN is summarized in Figure 2.
Dataset Collection and Synthesis. We collect real-world datasets from existing NA works and synthesize 7 additional datasets across 6 distinct domains. We follow the most classical method to synthesize NA datasets from a single network, where we insert 10% noisy edges into and delete 15% existing edges from the original network create permuted networks (Yang al., 2016; Zhang et al., 2020; Yan et al., 2021b; Zhang et al., 2021; Zeng et al., 2023a; Yu et al., 2025).
user for personalized recommen- dation (Zhang & Philip, 2015; Cao & Yu, 2016; 6; Liu et al., 2016; Chen et al., 2024a), PLANETALIGN includes 4 real-world datasets: Foursquare-Twwitee (Zhang & Philip. 2015), Douban (Zhang Tong, 2016), Flickr-LastFM (Zhang & Tong, 2016), and Flickr-MySpace (Zhang & Tong, 2016); for publication networks, where NA is used for author disambiguation (Li al., 2021), PLANETALIGN includes the most representative real-world dataset ACM-DBLP (Tang et al., 2008), and synthesizes additional datasets from Cora (Yang al., 2016) and ArXiv (Leskovec al., 2007); for biolog- ical networks, where NA uncovers hidden biological homologies by aligning proteins of different species (Singh et al., 2008; Clark & Kalita, 2014; Faisal et al., 2015), PLANETALIGN includes I real-world dataset SacchCere (Stark al., 2006; De Domenico et al., 2015b) and 2 synthetical datasets PPI (Zitnik & Leskovec, 2017) and GGI (Park et al., 2010). For knowledge graphs, where NA used for knowledge fusion (Sun et al., 2020: Liu al., 2023b; Chen al., 2023), PLANETALIGN includes 3 variants real-world dataset DBPISK (Sun et al., 2017), namely DBPI5K ZH-EN JA-EN and FR-EN. For infrastructure networks, where NA plays important role in cross layer
Specifically, for social networks, where NA is used align the
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Social Networks
Publication Networks
Biotogical Networks
Knowledge Graphs
Infrastracture Networks
Communication
Networks
Beal
Real
Foureguar
Twitter
AUM DULP
SarchCrree
x
DRPISK ZIE-EN
Buayy
Plone
Rxael
Deshan
Airport
EastFM
Flikkr My Spuce
ArXiv
GGI
DEPISK FRE-IN
PLANETALIGN
Consisteacy-hnee Methods
Embooding-hused
OT-huuse Methods
Teoktank
FINAL
DONE
REGAL
CresMNNA
NelTento
Wallze
BRIGHT NeNtalize WEAlig
St OTTlllze
PARBOOT
JOENA
Atterbbutel
O
superviccd
Figure 2: An overview of built-in datasets and NA methods in PLANETALIGN. For built-in datasets, indicate if they consist of attributed (Attr.) plain networks, and if they are real-world (Real) or synthetic datasets. For built-in NA methods, we indicate if they are designed for attributed or plain NA tasks, and if they are semi-supervised or unsupervised methods.
dependency inference (Yan et al., 2022), PLANETALIGN includes 1 real-world dataset Italy (Yan et al., 2022), and synthetic datasets Airport (Zhu al., 2021) and PeMS08 (Song et al., 2020). For communication networks, PLANETALIGN includes 1 real-world dataset Phone-Email (Zhang et al., 2017) and synthetic dataset Arenas (Kunegis, 2013). Detailed dataset statistics can be found in Appendix A.
Baseline Implementations. We implement 14 existing NA methods based on a unified API, in- cluding 2 representative consistency-baser methods, 8 embbeddnng methods, and 4 OT-based methods. Specifically, for consistency-bassel methods, PLANETALIGN includes IsoRank (Singh et al., 2008) and FINAL (Zhang & Tong, 2016); for embedding- methods, PLANETALIGN includes IONE (Liu et al., 2016), REGAL (Heimann et al., 2018), CrossMNA (Chu et al., 2019), NetTrans (Zhang et al., 2020), WAlign (Gao et al., 2021). BRIGHT (Yan et al., 202 NeXtAl- ign (Zhang et al., 2021), and WLAlign (Liu et al., 2023a); for OT-based methods, PLANETALIGN includes SLOTAlign (Tang et al., 2023), PARROT (Zeng et al., 2023a). HOT (Zeng et al., 2024a), and JOENA (Yu et al., 2025). Detailed introductions of built-in NA methods can be found in Appendix B.
4.2 UNIFIED AND EASY-TO-USE APIS
PLANETALIGN is carefully designed to provide unified and easy-to-use APIs to streamline the implementation, training, and evaluation of NA algorithms customizable datasets. An exam- ple usage of PLANETALIGN is shown in Figure 3. We also provide detailed documentation at https: / //pp/plaaa/ hplpanttalignn readthedoce 1c/ htt///n/aasss//////ee/ covering both quick-start tuto- rials and in-depth documentations of API usage of the major components of PLANETALIGN.
Specifically, train and evaluate NA algorithm on specific dataset, the user of PLANETALIGN will first define a PlanetAlign data Baseeata object and a Model object inherited from the base class PlanetAlign www.aaaigiiaiioo BaseModel For built-in datasets, PLANETALIGN provides downloading options and reproducible train/test split with a customized training ratio; for built-in algorithms, PLANETALIGN provides hyperparameter options upon definition of the algorithm, and unified API Py Torch for GPU/CPU offloading. Both built-in dataset and algorithm objects can be defined neatly in a single line of code. PLANETALIGN also provides unified and intuitive base classes for defining customized datasets and algorithms, as shown in Figure 3.
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Dataset
Train Test
Built-in NA dataset (0.8-, Douban)
output, alg.train(dataset-dataee
dataset Planetalign.drttsees.com rootwroot, download=True,
giós-gids, graph IDs
use_attrause__tt loggerslogger, **train_prrmmeters)
train train_ wtt.rrrttriinnatttte.... seedeseed)
ouput (nade exbeddings:
aliganent_nate pattinnnt_nattix..mmm logger: ..)
# Custonized NA dataset Planetalign.dan Batiiig Basebata(
result_dict alg. test(aatasetrdataset,
graphs=ggaphhs, dat. data. objects anchor linkss-ncchhn links, Tensor object train_rationtrainn retiostrainnratio seednseed)
gids-gids, # graph IDs use_attreuse_dt metrics-metries)
Algorithm
Logger
Built-in algorithn (e.g., FINAL)
logger PlanetAlign. logger. Teagger.Traingg Trainkogger( 1og_path--og. path
alg PlanetAligg www.s/aggoorsss.oao.ooo. FINAL
""hyprrpnanoterss.. 'cuda')
log nan.nnnneooooam nane, netrics-natrics, save-true)
Custonized NA algorithn
class M (PlanetAlign. www.:liiggnggggttnn.mmmmm Baseethh..asseodel)))
train():
Plat training of the selected netric logger-plad plot_curve(notriimeetre
def test (): Model (** wwwerppaaeterrss.ccoommmm toodccdda"
save save_path-savv_patte
Example usage of PLANETALIGN for benchmarking NA. Users begin by initializing dataset and algorithm objects, along with logger for training-time monitoring and visualization. The training and evaluation can then be performed through simple API calls with user-defined parameters, providing substantial flexibility in controlling the training and evaluation workflows.
Figure 3:
Before training an NA algorithm, the user has an option to initialize a PlanetAlign Logger
object used to log the training process of the algorithm. Then, the user can simply call the tra method of the algorithm object with the dataset and logger object, IDs of graphs be aligned, and additional configuration of training, c.g., training epochs, learning rate, etc., to start the training. Training outputs, including node embeddings, alignment matrix, and training performance returned by the train after the training process ends, providing fine-grained intermediate results of alignment that users can readily leverage for downstream tasks, such as cross-layer dependency inference (Yan al., 2022), knowledge integration (Yan et al., 2021a), and cross-KG modality fusion (Chen et al., 2023).
Finally, after the training process, the user can call the test method of the algorithm object with customized options of evaluation metrics. The optional logger object, which records and logs comprehensive data during training, also provides a rich collection APIs for visualizing the evolution of different metrics along training, e.g., training loss, time and memory usage, etc.
4.3 STANDARDIZED AND DIVERSE BENCHMARKING TOOLS
Standard Evaluation Metrics. PLANETALIGN provides low-level utility functions for computing standard and widely adopted evaluation metrics in the NA tasks, with custom options for alignment directions, such as left-to-right for pairwise alignment scenarios where the nodes G1 is aligned to G2 and vise versa. Specifically, PLANETALIGN includes the following metries:
Hits K. the case of aligning 91 to G2, Hits@ K refers to the proportion of nodes in G1 whose correct alignment in G2 is ranked within the top-K candidates by NA algorithm. Formally,
Hits K
Σ [rank, KK)
where N is the number of nodes in G1, rank; is the rank of the correct alignment for the i-th node G1. and { is the indicator function. Note that NA, Precision @ (Trung et al., 2020) is equivalent Hits@K.

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Mean Reciprocal Rank (MRR). MRR refers to the average reciprocal of the rank which the correct alignment appears in the candidate list. Formally, In the case of aligning J1 to G2,
N
MRR
N
rank;"
where N is the number of nodes in 91, rank; is the rank of the correct alignment for the i-th node in 91. Note that in NA, Mean Average Precision (MAP) (Trung et al., 2020) is equivalent MRR.
Diverse Benchmark Settings. The design of PLANETALIGN enables diverse and reproducible benchmarking of existing NA algorithms with minimal efforts, providing a rich collection of APIs and built-in utility functions that allow key dimensions of NA performance, including effectiveness, scalability, and robustness.
easily configure, run, and evaluate experiments along
Specifically, for effectiveness, PLANETALIGG supports custom training ratios and generates con- sistent, reproducible train/test splits by user-defined random seed, ensuring fair comparisons of different NA algorithms. The APIS also supports experiments that evaluate the sensitivity different NA methods with respect to the amount of supervision, providing valuable insights into their applica- bility various supervision regimes. PLANETALIGN further provides unified utility functions to selectively introduce or remove supervision, enabling side-by-side comparisons between supervised and unsupervised algorithms under the same setting; for scalability, PLANETALIGN includes built-in logging functionalities that automatically track the runtime and memory usage during training and inference, allowing consistent and transparent evaluation of the efficiency of NA algorithms across datasets of varying sizes; for robustness, PLANETALIGN provides utility functions for injecting edge- level, attribute-level, and supervision noise into input graphs, allowing comprehensive evaluation of the robustness of NA methods under diverse graph noises data inconsistencies.
EXPERIMENTS
Based on PLANETALIGN, we carry extensive experiments benchmark a wide range of NA algorithms across four key dimensions: effectiveness (Section 5.2). scalability (Section 5.3), robust- ness (Appendix D. and sensitivity supervision (Appendix D.2). Additionally, we compare our implementation to the official implementation of built-in NA algorithms of PLANETALIIGN to validate the correctness and efficiency our library (Appendix D.3).
5.1 EXPERIMENTAL SETUP
Datasets and methods. We benchmarks the performance of 14 NA algorithms on 18 NA datasets built into PLANETALIGN, as shown in Figure 2. Detailed dataset statistics and brief introduction to each algorithm can be found at Appendix and B, respectively.
Metrics. To evaluate effectiveness, we report Hits K and MRR introduced in Section 4.3. All reported Hits@ and MRR are averaged results from both alignment directions. To evaluate scalability, we report the runtime and peak memory usage.
Additional Setup. For each experiments, we run times and report the mean and standard deviation of the results. Additional experimental setup, such as the machine used to run the experiments and hyperparameter settings, are detailed in Appendix C.
5.2 EFFECTIVENESS RESULTS
We first evaluate the effectiveness of existing NA algorithms on plain networks under semi- supervised setting with 20% training ratio. Datasets randomly split for training and testing by fixed random seed to ensure fair and reproducible comparison. Table 2 shows the averaged results on all 18 datasets group by their categories. Detailed results plain and attributed NA datasets can be found in Appendix D.
We from Table 2 that OT-based methods, particularly PARROT (Zeng al., 2023a) and JOENA (Yu et al., 2025), consistently achieve SOTA alignment performance in Hits @ K and MRR across all datasets, demonstrating the effectiveness of optimal transport in aligning distributional
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Table 2: Effectiveness and efficiency results of NA algorithms on plain networks with training ratio of 20%. We group the 18 datasets in PLANETALIGN by their categories and report the averaged
Hits@1, 1, @its@@ 10, MRR (in %). inference time (Time), and peak memory usage (Memory). Cells that contain the 1st/2nd/3rd best results are highlighted in red/blue/gee respectively. Detailed results for each dataset can be found in Appendix D.5.
Dutaset
Social
Publication
Biologkcl
Knowledge
Infrastructure
Communication
Metries
His@1
1iis@10 MRR [lis@1
@1 MRR [lis@1
1iis@10 MRR
llis@ 10 MRR [itv@1 llis@@ to MRR [liv@1
lits@ MRR
IsoKauk
4.2
18.9
59.1
31.4
21.6
45.1
29.4
11.5
50.0
24.1
44.5
FINAL
4,9
22.3
10:1
22.3
68.6
37.3
22.9
56.6
34.1
13.9
55.4
27.3
15.1
53.6
28.0
21.7
57.7
33.9
IONE
7,9
20.0
12.1
28.7
63.6
40.1
46,1
60.5
51.0
4.7
20.3
10.0
29.6
51.2
37.00
50.4
69.0
56.7
REGAL
0.3
2.2
1.2
1.8
7.8
3.9
1.0
5,3
2.6
0.8
2.9
1.6
2.8
13.4
6.7
45.3
49.5
47.2
CresMNN
1.2
11.1
4.5
13.2
58.1
27.2
40.2
58.7
46.5
2.7
28.8
10.7
14.6
30.9
20.2
22.8
53.3
33.9
NetTruns
7.2
11.9
77.3
52.7
34.2
41.8
28.8
62.8
29.3
39.6
45.2
62.6
WAlign
4.2
8.5
6.1
31.2
49.6
37.8
20.3
27.3
22.9
19.4
28.2
22.8
29.1
47.3
35.5
49.6
56.0
52.1
BRIGHT
5.1
17.0
9.0
40.4
74.0
51.8
30.5
48.0
36.5
30.4
61.7
40.9
29.9
57.0
39.5
50,9
62.3
55.0
NeXtAlign
7.1
19.5
11.3
43.2
76.9
54.7
25.9
44.8
32.8
27.5
59.9
38.3
28.0
55,1
37.8
29.5
51.4
37.2
WLAllgn
7.6
43.2
41.2
41.3
25.9
44.2
PARROT
12.6
26.3
17.2
66.6
88.6
74.4
61.6
73.4
65.5
66.0
87.2
73.
51.8
69.2
57.8
63.3
86.7
71.3
SL.OTAlig
0.9
4.0
2.2
50.7
65.5
56.1
48.6
54.5
50.7
1.5
5.8
3.1
53.2
601.8
55.7
49.3
52.6
50.9
HOT
5.3
16.0
5.2
38.
1
65.6
23.7
25.4
37.9
15.2
33.9
61.2
21.4
32.1
52.1
19.5
52.1
66.2
28.5
JOENA
18.7
35.1
24.4
73.2
92.1
80.2
63.7
72.9
66.8
66.3
87,8
73.0
62.9
75.0
67.2
66.3
89.0
74.3
Datavt
Social
Publication
Biological
Knowledge
Infraddrctture
Commonication
Metries
Timets)
Timeis)
MemoryyGBB
Timels)
Memory(Gl)
Timets)
Memory(GB)
Timsess)
Memory(Ga))
Timeis)
Memory
BooRank
25.17
3.54
$7.99
6.89
13.01
2.67
1.54xx107
15.97
0.28
0.66
0.17
0.80
FINAL
5.91
5,39
6.75
10.06
1.88
3.54
18.10
24.37
0.10
0.80
0.11
0.88
IONE
6.34x100
1.43x100
4.16
x,4
1.93
1.50x 10
8.75
9.41 10"
0.90
8.10x100
REGAL
9.38
1.16
16.14
3.18
7.28
1.55
30.83
5.96
0.76
0.81
1.17
0.77
CrosMIN
3,06x10'
1,40
L16x100
3.16
5.33x10'
1.58
1
6,14
59.03
0.98
5.78x10P
0.81
NetTrans
1.56x110
8.88
5.14x.10P
8.57
1,08x 10?
3.90
x.6Px10P
21.90
6.46
1.28
12.37
1.54
WAllgn
0.61
2.65
9:11
9.88
2/16
3.86
10:05
15:40
0.12
1.43
0.20
1.11
BRIGHT
21.76
1,26x 10
3.66
33.55
3:24
3.81 10
11.53
0.28
1.14
0.33
NeNtAlign
40.89
3.75
L 10
7,82
19.35
3.57
2,62x10*
13.57
0.29
1.34
2,44
0.99
WI.Aligs
7A1x110
2.17
2.69x 10
11.98
1115x10*
4.21
3.24x 10
28.33
4.25xz 10
1.35
6.99x10P
0.95
PARROT
x.99xx10
11.68
$4.63
10
28.47
0.76
0.90
SLOTTAlige
46.31
6.40
5 64x 10
11:55
67 66
3.96
10
28.27
3.85
1.03
1.23
0.80
HOT
4.01x102
3.85
7.89x 10P
7.75
7,08 102
4.44
100
18:43
8.95
2.12
7.04
4.46
58.73
30.60
2.73
3.65
6.61
26.47
0.05
1.02
structures. Embedding- methods such as IONE (Liu et al., 2016), NetTrans (Zhang al., 2020), and BRIGHT (Yan et al., 2021b) can be effective in aligning some networks. However, their strong performances are not consistent across different datasets, potentially due to the space disparity issue (Yan et al., 2021b; Zhang et al., 2021). Consistency- based methods, while occasionally perform well on certain datasets, usually outperformed by best-performing embedding- and OT-based methods, suggesting that relying solely on consistency principles may lead to sub-optimal alignment.
In addition empirical observations, we further provide theoretical analysis into the superior performance of ba-ed alignment methods. Compared with consistency-basee methods, which are restricted by local consistency principles. OT-based methods go beyond local assumptions by solving a globally constrained optimization problem. Compared with embedding- based methods, which infer alignment from noisy embedding similarities, O'T-based methods directly learns a robust alignment matrix from the transportation cost, thanks to the marginal constraints that naturally encourage one-to-one node alignment. Empowered by constrained optimization and informative transportation cost encoded by powerful graph proximity measures or learnable node embeddings, ba-bdsed methods learns robust, deterministic, and global cetooll-ttucuure-aae aware alignment.
Takeaway #1: Optimal transport demonstrates significant potentials in NA.
Best-performing OT- methods consistently outperform consistency and embedding -based approaches by a significant margin in alignment performance across diverse domains, demon- strating the power of constrained optimization und informative transport cost which lead to robust, deterministic, and klohal-structure-awaar alignment.
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WAllgn
REGAL
BRIGHT
PARROT
HOT JOENA
IsRaan
FINAL
Cress/NNA
NeXtAliig
SLOTAlign
Naansans
WLAlign
Consistency-base Methods
Embedding-basee Methods
OT-based Methods
Runtime (9)
10
0.2
0.6
1.0
1.6
2.0
0.0
0.6
1.0
1.4
1.0
0.0
0.4
1.4
1.8
(x109)
#Nades (x109)
(x109)
Consistency-based Methods
Embeddiig-based Methods
OT-based Methods
40-
Peak Memory (G8)
20-
12 (x109)
1.8
0.8 1.2
1.8
0.8
18
(x109)
1x
Figure Scalability results on ER graphs. The x-axis shows the number nodes the ER graphs (in 10*), and the y-axis of the 1st/2nd row shows the inference time and peak memory usage, respectively.
5.3 EFFICIENCY AND SCALABILITY RESULTS
We also include the efficiency results of NA algorithms plain networks under semi-supervise setting with a 20% training ratio in Table 2. To further evaluate the scalability of NA algorithms, we conduct another set of experiments et al., 1960) model with a fixed average node degree of 10 under the same semi-supervised setting, and record the inference time and peak memory usage the number of nodes increases in Figure 4.
synthetic graphs generated by the Erdös- Rényi (ER) (Erdos
Embedding-bart based methods typically face a two-out-offthe -f-hree trade-off among effectiveness, time efficiency, and memory efficiency. Specifically, WAlign (Gao al., 2021) and BRIGHT (Yan et al., 2021b) are among the most scalable algorithms in terms of inference time thanks to simple neural network (NN) structures which only requires forward pass during inference. However, they tends be less scalable in memory usage due to overheads of NN parameters. In terms of memory usage, CrossMNA (Chu al., 2019) and IONE (Liu al., 2016) achieve the best scalability as they learn low-dimensional embeddings without using NN. However, they tends to be less scalable in time since their transductive embeddings requires retraining for different networks (Hamilton et al., 2017). REGAL (Heimann et al., 2018) achieve both time and memory scalability by decomposition sampled embedding matrices but is less effective compared to other NA methods.
Takeaway #2: Embedding-based methods face two-out-of-three trade-off among effec- tiveness, time efficiency, and memory efficiency.
Embedding- bbsed methods face trade-off among transductive embeddings for memory efficiency, inductive embeddings for time efficiency, and learning-base based approaches for effectiveness.
Consistency-basen methods (Singh et al., 2008; Zhang & Tong, 2016), on the other hand, scale moderately in terms of both time and memory usage. OT-based methods, in general, share similar methods since the optimizations of both kinds of methods
scalability results as consistency-bast involve matrix operations of quartic complexity. Although the original OT problem is non-convex and computationally expensive to solve by gradient descent (Tang et al., 2023), PARROT (Zeng et al., 2023a) and JOENA (Yu et al., 2025) solve the OT problem efficiently by convex approximation (Peyré et al., 2019) and proximal point methods (Xu et al., 2019a). HOT (Zeng et al., 2024a) further utilizes hierarchical OT framework for cluster-level alignment scale efficiently large networks.

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Published as a conference paper at ICLR 2026
Takeaway #3: OT-based methods requires efficient optimization methods to scale similarly as consistency-basee methods.
OT-based methods requires efficient optimization of OT problem, e.g. convex approximation, to scale moderately like consistency-baud methods in terms of both time and memory usage.
5.4 ROBUSTNESS AND SENSITIVITY RESULTS
We evaluate the robustness of NA methods under various types of graph noises, as well as their sensitivity to different levels of supervision. Our key findings are twofold. First, different NA methods show distinct sensitivities to different types of graph noises, suggesting that effective integration of different alignment techniques can potentially improve the overall robustness of NA algorithms. Second, current NA algorithms remain sensitive to supervision, underscoring the need for future research on self-supervised and active alignment approaches. Due to space constraints, detailed experimental results, analysis, and key takeaways are provided in Appendix D.1 and D.2.
6 CONCLUSION
In this paper, we introduce PLANETALIGN, a comprehensive library that facilitates the benchmarking and development of network alignment methods. PLANETALIGN highlights a collection of 18 differ- ent public datasets spanning 6 different domains, along with a unified and efficient implementation of 14 different NA algorithms of different categories. With a comprehensive list of evaluation metrics, benchmarking tools, and utility functions implemented as easy-to-use APIs, PLANETALIGN not only enables fair and reproducible benchmarking of NA algorithms but also facilitates the development of new NA methods. Through extensive benchmark, we reveal practical insights into the strengths and limitations of existing NA methods which guides the development of future NA algorithms.
7 LIMITATIONS AND FUTURE WORK
While we introduce a comprehensive library for benchmarking NA, PLANETALIGN could be poten- tially improved from the following two directions. First, although PLANETALIGN features rich collection of baselines, some variants of NA methods that targets a specific kind of network remain uncovered, e.g., entity alignment approaches (Chen al., 2023; Liu al., 2023b; Yan et al., 2021a) for aligning knowledge graphs. Second, PLANETALIGN focuses primarily on benchmarking pairwise NA problems. Although multi-network alignment methods are included in PLANETALIGN (Chu et al., 2019; Zeng et al., 2024a), benchmarking under a simultaneous multi nulti-network alignment setting remains underexplored at this stage.
As for future work, we will continuously expand PLANETALIGN to incorporate new NA datasets, algorithms, benchmark settings, and utility functions. Specifically, for NA datasets, we plan to include 1) multi-network alignment datasets which consist of more than two networks, such as multi-layered version of ArXiv (De Domenico et al., 2015a), Twitter (Omodei et al., 2015), and SacchCere (De Domenico et al., 2015b), 2) dynamic networks which evolves over time, such as synthetic datasets from (Vijayan et al., 2017) and (Yan et al., 2021a), and 3) cross-domain datasets which consist of networks from different domains, such as text-image network constructed by GOT (Chen et al., 2020); for NA algorithms, we plan to introduce 1) domain-specine alignment algorithms, such entity alignment methods DualMatch (Liu et al., 2023b) and MEAffomer (Chen et al., 2023), 2) multi-network alignment algorithms such as MrMine (Du & Tong, 2019), 3) dynamic NA algorithms such DynaMAGNA++ (Vijayan et al., 2017) and DINGA (Yan et al., 2021 for benchmark settings, we plan to add additional evaluation metrics for measuring the uncertainty of the aligmment (Zhou al., 2021), which are critical for developing active or self- self-improving NA methods highlighted as important future directions in our paper; for utility functions, our immediate goal is to introduce scalability tools to allow easy acceleration of NA algorithms built upon PLANETALIGN, including distributed training APIs, sparse and low-rank matrix optimizations, and low-rank (Scethon & Cuturi, 2022) & sliced OT (Liu et al., 2024b) optimization tools.
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ACKNOWLEDGMENTS
This work is supported by NSF (2316233) and AFOSR (FA9550-24-1-00022 The content of the information in this document does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
ETHICS STATEMENT
Our library uses only publicly available datasets and conducts evaluation in a transparent and responsible manner in accordance with the code of ethics of ICLR. The research does not involve human subjects, animal studies, any other procedures that may raise ethical concerns.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, for datasets in PLANETALIGN, we include their detailed statis- ties and description in Appendix A. For experimental setup, we include detailed de- scription of adopted evaluation metrics, machines, dataset splits, and hyperparameter set- tings in Section 5.1 and Appendix C. The source code of PLANETALIGG is avail- able htttp:://iithui com hot..//-eleooo/llaaeallliioe htw.hign.rraaatddeeeeeedeooee wwtpeeeawwww...eeeeeeeeeeeeeeeee wttp:///s...eeeeeetsts/s///
with detailed documentation at
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TABLE OF APPENDIX
Datasets Details
19
A.1 Dataset Statistics
19
A.2 Dataset Descriptions
19
Network Alignment Methods
B.1
Consistency-based Methods
21
B.2 Embedding-basse Methods
B.3 OT-based Methods
22
Detailed Experimental Setup
22
Additional Experimental Results
22
D.1 Robustness Results
22
D.2 Sensitivity to Supervision Results
................................
24
D.3 Comparison with official implementations
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D.4 Scalability Results on Large Graphs
26
D.5 Detailed Effectivvnness Results
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Statement of LLM Usage
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A DATASETS DETAILS
A.1 DATASET STATISTICS
Table
Dataset Statistics.
Domain
Networks
# nodes
#edges
# node attr.
# edge attr.
Type
Foursquare
5,313
54.233
worldworld
Twitter
5.120
130.575
Douban(online)
3,906
8,164
538
Real-world
Douban(offline)
1,118
1,511
538
Social Networks
Flickr
12.974
16.149
3
Real-world
Lastim
15,436
16,319
Flickr
6,714
7,333
Real-world
Myspace
10,733
11,081
Phone
1,000
41,191
0
Real-world
Email
1,003
4,628
0
Communication Networks
Arenas
1.135
10,902
Synthetic
Arenas2
1.135
10,800
ACM
9,872
39,561
17
Real-world
DBLP
9,916
44,808
17
Coral
2.708
6,334
1,433
Publication Networks
Synthetic
Cora2
2,708
4,542
1,433
ArXivl
18,722
217,921
Synthetic
ArXiv2
18,722
168,394
SacchCerel
5,928
66,150
0
Real-world
SacchCere2
5,042
29,599
PPI1
3,480
117.429
Biological Networks
Synthetic
PP12
3,480
90,741
GGIT
10,403
115,755
0
Synthetic
GGI2
10,403
89,448
0
DBP15K /H
19,388
70,414
300
Real-world
DBP15K_EN
19,572
95,142
300
DBP15K JA DBP15K_EN
19,814
77,214
300
Knowledge Graphs
Real-world
19,780
93,484
300
DBP15K_FH FR
19,661
105,997
300
Real-world
DBP15K_ER EN
19,993
115,722
300
Italy1 Italy2
349
416
Real-world
349
435
Airpor1
1.190
14.958
4
Infrastructure Networks
Synthetic
Airpur12
1.190
11,560
4
PeMS08-1
170
301
Synthetic
PeMSO8-2
170
233
A.2 DATASET DESCRIPTIONS
Detailed datasets descriptions are introduced as follows
Foursquare-Twe Twitter (Zhang & Philip, 2015). pair of online social networks, Foursquare and Twitter. Nodes represent users and an edge exists between two users if they have follower/followee relationships. Both networks are plain networks. There are 1,609 common users across two networks.
Douban (Zhang & Tong, 2016). A pair of online-offline social networks constructed from Douban. Nodes represent users and edges represent user interactions the website. The location of user is treated as the node attribute, and the contact/friend relationship are treated as the edge attributes. There are 1,118 common user across the networks.
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Flickr-LastFM (Zhang & Tong, 2016). A pair of social networks from Flickr and LastFM. Nodes in both networks represent users, and edges represent friend / following relationships in Flickr and LastFM, respectively. The gender of user is treated as the node attributes (male, female, unknown), and the level of people a user is connected to is treated as the edge attributes (e.g.. leader with leader). There are 452 common users across two networks.
Flickr-MySpace (Zhang & Tong, 2016). A pair of social networks from Flickr and MySpace. Nodes in both networks represent users, and edges represent friend following relationships. The gender of a user is treated as the node attributes (male, female, unknown), and the level of people a user is connected to is treated as the edge attributes (e.g.. leader with leader). There are 267 common users across two networks.
ACM-DBLP (Tang et al., 2008). A pair of undirected co-authorship networks, ACM and DBLP. Nodes represent authors and edges an edge exists between two authors if they co-author at least one paper. Node attributes are available in both networks. There are 6,325 common authors across two networks.
Cora (Yang et al., 2016). A pair of networks synthesized from the citation network Cora. Each network Nodes represent publications and an edge exists between two publications if they have citation relationship. The two networks are noisy permutations of the original network generated by randomly inserting 10% edges (Coral) and deleting 15% edges (Cora2) from the original network, respectively. There are in total 2, 2,708 common nodes across two networks.
ArXiv (Leskovec et al., 2007). A pair of networks synthesized from the Arxiv ASTRO-PH (Astro Physics) collaboration network (Leskovec et al., 2007). Nodes represent authors and an edge exists between two authors if they have co-authored paper. The two networks are noisy permutations of the original network generated by randomly inserting 10% edges (ArXiv1) and deleting 15% edges (ArXiv2) from the original network, respectively. Node and edge attributes are not available. There are in total 18,722 common nodes across two networks.
SacchCere (Stark et al., 2006; De Domenico et al., 2015b). A pair of direct interaction layer and association layer from the SacchCere multiplex GPI network. The SacchCere network consider different kinds of protein and genetic interactions for Saccharomyces Cerevisiae in BioGRID (Stark al., 2006), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. There are in total 1,337 common nodes across two layers of networks.
PPI (Zitikk & &eekkoee, 2017). A pair of networks synthesized from the protein- protein-protein protein interaction (PPI) network (Zitnik & Leskovee, 2017). where nodes represent human proteins and edges represent physical interaction between proteins in a human cell. The immunological signatures are included as node features. The two networks are noisy permutations of the original network generated by randomly inserting 10% edges (PPI1) and deleting 15% edges (PP12) from the original network, respectively. There are total 3,980 common nodes across two networks.
GG1 (Park et al., 2010). A pair of networks synthesized from the human gene-gen gene interaction (PPI) network from IsoBase (Park et al., 2010). Nodes represent human genes and edges represent gene-gene interactions. The networks are noisy permutations of the original network generated by randomly inserting 10% edges (GGI1) and deleting 15% edges (GGI2) from the original network, respectively. There are in total 10.403 common nodes across two networks.
DBPISK ZH-EN, JA-EN, FR-EN (Sun et al., 2017). 7)). Pairs of Chinese, Japanese, and French to English version of multi- lingual DBpedia networks. The node attributes are given by pre-trained and aligned monolingual word embeddings (Xu et al., 2019b). There are 15,000 pairs of aligned entities in DBP15K ZH-EN (Chinese to English), JA-EN (Japanese to English), and FR-EN (French to English), respectively.
Italy (Yan al., 2022). pair of power grid networks from two regions in Italy. Nodes represent power stations and edges represent the existence of power transfer lines. Node attributes are derived from node labels. There are in total 377 common nodes across two networks inferred from the ground-truth caoss-layer dependencies.
Airport (Zhu et al., 2021). A pair of networks synthesized from the American air- traffic net- work (Ribeiro et al., 2017). Nodes represent airports and edge exists between two aiports if there are commercial flights between them. The level of activity in each airport is used as node attributes. The two networks are noisy permutations of the original network generated by randomly inserting 10% edges (Airport1) and deleting 15% edges (Airport2) from the original network, respectively. There are in total 1,190 common nodes across two networks.
PeMS08 (Song et al., 2020). A pair of traffic networks synthesized from the Performance Mea- surement System (PeMS) Data Source. Nodes represent sensors and edges indicate traffic flow
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correlation. Node attributes averaged across all time interval. The two networks noisy permutations of the original network generated by randomly inserting 10% edges (PeMS08-I) and deleting 15% edges (PeMS08-2) from the original network, respectively. There in total 170 common nodes across two networks.
Phone-Fmail (Zhang al., 2017). A pair of communication networks among people via phone or email. Nodes represent people and an edge exists between two people if they communicate via phone or email least once. Phone network includes 1,000 nodes and 41,191 edges. Email network includes 1,003 nodes and 4,627 edges. Both networks are plain networks. There are 1,000 common people across two networks.
Arenas (Kunegis, 2013). A pair of networks synthesized from the email communication network Arenas at the University Rovira Virgili. Nodes are users and each edge represents that at least one email was sent. The two networks are noisy permutations of each other. There are in total 1,135 common nodes across two networks.
B NETWORK ALIGNMENT METHODS
B.1 CONSISTENCY-BAN BASSD METHODS
IsoRank (Singh et al., 2008). IsoRank is originally designed for global alignment of multiple PPI networks. It is built upon neighborhood topology consistency which assumes that the neighbors of aligned anchor nodes should be aligned well, and formulated an eigenvalue problem. (Yan et al., 2021b) reveals that the formulation of IsoRank can be considered as conducting random walk propagation of anchor links on the product graph to achieve topology consistency.
FINAL (Zhang & Tong, 2016). FINAL interprets the alignment consistency principles as an optimization problem and introduces additional consistency principles at node/edge attribute levels to handle attributed network alignment.
B.2 EMBEDDING-BASER METHODS
IONE (Liu et al., 2016). IONE modeled the follower/followee-shee of different nodes as input/out- put context vectors to learn rrximity--prsservi node embeddings, and solve the node embedding and network alignment problem based on a unified framework.
REGAL (Heimann et al., 2018). REGAL designs embedding learning methods called xNetMF which learns powerful node embeddings by matrix factorization on a linear combination between cross-network structural and attribute similarity matrix. Based XNetMF embeddings, REGAL infer node-level alignment of two networks based on Euclidean distance of nodes in the embedding space.
CrossMNA (Chu et al., 2019). CrossMNA leverages cross-network structural information to learn inter and intra network embeddings simultaneously. By comparing inter network embeddings across different networks, CrossMNA is capable of aligning multiple networks at the same time. NetTrans (Zhang et al., 2020). NetTrans approach the network alignment problem from a cross- network transformation perspective. It learns the transformation of both network structure and node attributes at different resolutions to identify node-level alignment.
WAlign (Gao et al., 2021). WAlign learns node embeddings by a lightweight GCN model to capture both local and global graph patterns and proposes a Wasserstein distance discriminator to minimize the Wasserstein distance between node embeddings across different graphs.
BRIGHT (Yan et al., 2021b). BRIGHT first generate positional node embeddings by random walk with restart (RWR) (Tong et al., 2006) against anchor links. To handle plain network alignment, BRIGHT-U learns position-aware embeddings by transforming RWR embeddings through a shared MLP. To handle attributed network alignment, BRIGHT-A use shared GCN model for transforming node attributes and concatenates the output embeddings with RWR vectors before feeding into the shared MLP.
NeXtAlign (Zhang et al., 2021). NeXtAlign designs a spatial GCN model and learns node embeddings that balance the alignment consistency and disparity by crafting the sampling strategy. WLAlign (Liu et al., 2023a). WLAlign proposes cross-network Ceisfeiler-eehmal relabeling scheme to learn embeddings that preserves long-range connectivity to the anchor pairs on plain networks.
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B.3 OT-BASED METHODS
SLOTAlign (Tang et al., 2023). SLOTAlign utilizes a parameter-free GCN model to encode graph structure. By integrating output embeddings of multiple layers of GCN through a learnable linear combination, SLOTAlign encode the Gromov- Momov-Wasserstein distance between two networks via the learned embeddings and optimize the embedding and optimal transport problem alternatively to infer alignment.
PARROT (Zeng et al., 2023a). PARROT encodes a position-aware transportation cost by random walk with restart (RWR) (Tong et al., 2006) on separate and product graphs, and integrate consis- tency principle at node, edge, and neighborhood levels into the optimal transport formulation. Then, it solves the resulting optimization problem efficiently via constrained proximal point methods to infer node-level alignment.
HOT (Zeng et al., 2024a). HOT proposes hierarchical multi-marginal optimal transport framework which first decomposes multiple networks to aligned clusters via the fused Gromov- Wasserstein (FGW) barycenter (Peyré et al., 2016) and then aligns node in aligned clusters simulta- neous by solving optimal transport problem within clusters.
JOENA (Yu et al., 2025). JOENA transforms the transport plan of optimal transport into an adaptive sampling strategy via a learnable transformation to learn node embeddings and alignment in mutual beneficial manner.
C DETAILED EXPERIMENTAL SETUP
Machine. All experiments conducted computing server equipped with dual Intel Xeon@ Gold 6240R CPUs and 4 NVIDIA Tesla V100-SXM2 GPUs with 32GB memory each.
Dataset split and hyperparameters. To mitigate the randomness introduced by single random dataset split, we report the average metrics of different dataset split based on 5 randomly selected seeds. All NA methods are evaluated under the same dataset splits ensure fair comparison. For each dataset split, we run a NA algorithm 5 times and report the average metrics. Hyperparameters tuned with fixed budget of per key parameter based the default values and hyperparameter study in the original papers. Detailed hyperparameter search spaces can be found in Table 4.
Table 4: Hyperparameter search spaces.
NA Method
Search Parameters
IsoRank
a (0.1,0.3. 0.5, 0.7.
FINAL
e 0.31 0.5, 0.7,
IONE
out_ dim_ (32, 64, 100, 256.
REGAL
kg (1,5, 10, 15, 20).
layersE 11,5,,,455).o (0.001, 0.000,
CrosMNN
d, é (10, 50, 100, 150, 200), d, E 500,50,100 50, 100, 150, 200)
NetTrans
a [0.01,0.1. 1, 100), 3 (0.01. 1, 100), Y (0.01. 10, 100). (1,2,3,4.5)
WAlign
b (128, 256. 512, 1024, 2048),
(0.01, 0.000,..0000 0.06, 0.060
BRIGHT
8 (0.05. 0.1, 0.15, 0.2, 0.25), outtoot_iie dime (32, 64, 128, 256, 512). neg_ reg_sample sizeÉ (100, 300, 500, 900)
NeXtAlign
3 (0.05, 0.1, 0,15, 0.2, 0.25), out dime (32, 64, 128, 256, 512). neg sig__samll__saee (100, 300, 500, 700, 900)
WLAlign
dit_iime [32, 128, 256. 512), neg_sample____ee sizee (20,
100)
PARROT
E (..1, 1, 10). A, nAdthot A, E q A, Apdetuiit
SLOTAlign
E (0.001, 0.005, 0.01, 0.1),
sizee 3,,,5,4555
HOT
E (0.001, 0.005, 0.01, 0.1). a (0.1, 0.3, 0.5, 0.7.
JOENA
0.1.1.0..,05007770.3 % (0.001, 0.005, 0.01, 0.05, 0.1). (0.1.0.5 10,
D ADDITIONAL EXPERIMENTAL RESULTS
D.I ROBUSTNESS RESULTS
To benchmark the robustness of existing NA algorithms, we conduct controlled experiments study the impact of edge, attribute, and supervision noises to alignment performance, offering practical insights into the development of robust NA methods.
Edge noise. We introduce edge-level noise to simulate real-world edge perturbation (Jin et al., 2020). Specifically, the p% edge noise level is defined as randomly adding/deleting p% edges in
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IONE REGAL
CressMNA
WAllgn BRIGHT
NextAlign
SLOTAlign PARROT
HOT JOENA
FINAL
NetTrans
WLAlign
Social
Publication
Blologl cal
Infrastructure
Comm Communication
Knowlooge
C.A.
5-6 0.
02
24
36
64
C2 24
of 24
82 G4
C6
A2 14
60
02 24
64
4000
C4
05
24 Nobe
03
attibutt
Atritute
24
S.pervaii Neris Level
Supenteion hotes Lavel
Superviiion Noha Level
Superaalo Nois uevel
Superca on Hoiss Levil
Supenteon Moton Level
Figure 5: Robustness results of NA methods under different levels of edge, attribute, and supervision noises on representative datasets across 6 domains. The x-axis of the plots in the 1st/2nd/3rd row shows the noise level of cdge/aatribute/superviiio respectively, and the y-axis shows the MRR.
the second network be aligned (Tang et al., 2023; Zeng et al., 2023a). We conduct evaluations of all NA methods under semi-supervised (20% training ratio) plain NA setup to avoid potential interference of node/edge attributes.
Attribute noise. We introduce attribute-level noise simulate real-world attribute perturba- tion (Zheng et al., 2021). Specifically, the p% attribute noise level is defined as randomly perturbing p% node and edge attributes in the second network to be aligned (Zeng et al., 2023a). We conduct evaluations of attributed NA methods under semi-supervised attributed NA setting with a training ratio of 20%.
Supervision noise. We introduce supervision noise evaluate the robustness of semi-supervised NA methods against noisy anchor node pairs (Yan et al., 2021b; Tang et al., 2023). Specifically, the p% supervision noise is defined as randomly setting p% anchor nodes in the second graph to non-anchor nodes. To ensure fair comparison, we only evaluate the robustness of semi-supervised attributed NA methods against supervision noise under a semi-supervised attributed NA setting with training ratio of 20%.
Analysis. Robustness results on five representative datasets are shown in Figure 5. Firstly, for edge noise, consistency- basse methods, including IsoRank (Singh et al., 2008) and FINAL (Zhang & Tong, 2016), are among the most robust methods with the slightest performance drop across all methods (Liu et al., 2016; Heimann et al., 2018; Chu et al., 2019; Zhang et al., 2020; Gao et al., 2021; Yan et al., 202 Zhang et al., 2021; Liu et al., 2023a), while slightly approaches, generally show descent performance degradation
datasets. Embedding
less robust than consistency- ratio edge noise level increases. OT-based methods (Zeng al., 2023a; Tang et al., 2023; Zeng et al., 2024a; Yu et al., 2025), on the other hand, differ significantly in terms of robustness to edge- level noisel, indicating that although OT can reduce the negative effect of graph noises by marginal constraint (Zeng et al., 2023a; Yu et al., 2025), they require careful design of the transportation costs avoid noise amplification during optimization. Nevertheless, OT-based methods PARROT (Zeng et al., 2023a) and JOENA (Yu et al., 2025) consistently outperforms all other NA algorithms in alignment performance across different noise levels.
"We flip binary attributes and add standard gaussian noise into normalized continuous attributes. We include FINAL (Zhang & Tong, 2016) which has semi-supervised version in its original paper.
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Published as a conference paper at ICLR 2026
PARROT
JOENA
FINAL
Netirans
WLAlign
HOT
Social
Publication
Biologi
Infrastructure
Communication
Tran Kadls
Toan Raii
Ton Rrl
Tell 120.0
Toan Aatie
Taan Roio
T1281 Kull
Tran Hallo
Tun Nic0
Talt 1o0.0
Tan
Figure 6: Sensitivity results of semi-supervised NA algorithms to different levels of supervision. The two rows of plots correspond to plain (upper row) and attributed (lower row) NA settings respectively. The x-axis shows the training ratio (i.e., supervision level), and the y-axis shows the MRR.
Secondly, for attribute noise, PARROT (Zeng et al., 2023a) and JOENA (Yu et al., 2025) becomes the most robust algorithms across all datasets. While both methods are OT-based, PARROT integrates consistency principles which further improve its robustness, and JOENA adopts an embedding- encoded OT cost learned via MLP for robust alignment. Consistency-basel methods remain robust to attribute noise on most datasets. Embedding- based methods are generally more sensitive to attribute noise than edge noise and suffer from significant performance drop under high attribute noise level, which highlights the need for more robust embedding learning approaches, potentially through the integration of optimal transport or consistency principles.
Finally, for supervision noise, the performance of most NA algorithms degrades significantly as the noise level increase, indicating that the effectiveness of existing semi-supervised NA methods rely heavy on the quality of anchor node pairs even when node/edge attributes are available. Nevertheless, JOENA (Yu et al., 2025) consistently shows the mildest performance drop across all datasets, demonstrating the power of effective combination of embedding and OT-based methods. Future methods may explore more effective integration of consistency, embedding, and OT-based approaches to better handle different kinds of real-world noise.
Takeaway #4: Different NA methods are sensitive to different kinds of noises. Effective integration of different NA techniques could be a way out.
Different NA methods may be sensitive to different kinds of real-world noises. Integrating different NA techniques effectively, such as consistency principles, embedding learning, and optimal transport, could potentially improve the overall robustness of NA algorithms.
D.2 SENSITIVITY TO SUPERVISION RESULTS
To comprehensively evaluate the impact of supervision on the performance of NA algorithms, we conduct set of experiments to study the sensitivity of semi-supervised NA methods to different levels of supervision. Specifically, we gradually increase the training ratio and report the MRR of semi-supervised NA methods five representative datasets under both plain and attributed NA settings. The results are presented in Figure 6.
Analysis. Firstly, the performance of NA algorithms generally shows a growing trend as the training ratio increases, with only a few exceptions such as JOENA (Yu et al., 2025) on Douban (Zhang & Tong, 2016), potentially due to overlitting on training data or the presence of noisy anchor pairs from real-world datasets. Nonetheless, most NA methods benefit significantly from increased supervision, demonstrating its importance to the effectiveness of NA algorithms.
Secondly, the of attribute information typically improves the performance of NA algorithms under low supervision. However, the performance gap between plain and attributed settings narrows as the training ratio increases. For example, PARROT (Zeng al., 2023a) achieve MRR of
24

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Published as a conference paper at ICLR 2026
approximately 0.7/0.3 Douban with/without attribute information under 10% training ratio, whereas the performance rises to about 0.95/0.9 under a 90% training ratio. This suggests that while attributes help in supeevieinn scenarios, increasing supervision remains crucial even in the presence of node and edge attributes in graphs. Combined with our previous robustness study against supervision noise, present the following findings:
Takeaway #5: Supervision greatly affect the effectiveness of NA algorithms.
The quality and quantity of supervision greatly affect the performance of NA algorithms even in the presence of node and edge altributes, suggesting that supervidd and active learning methods that discover high-quus high-quality anchors could a promising directions for NA research.
D.3 COMPARISON WITH OFFICIAL IMPLEMENTATIONS
Table 5: Performance and runtime comparison with official implementations averaged against all datasets, represents the absolute difference between official and PLANETALIGN' implementation.
Metrics
MRR
Training Runtime(s)
Version
Official
PLANETALIGN
A
Official
PLANETALIGN
A
Specdup
REGAL
0.079
0.080
+0.001
20
14
-6
1.43
CrossMNA
0.220
0.222
+0.002
298
210
-88
1,42
NetTrans
0.373
0.374
+0.001
919
817
-102
1.12
WAlign
0.271
0,270
-0.001
79
68
-11
1.16
BRIGHT
0.362
0.362
+0,000
768
619
-149
1.24
NeXtAlign
0.391
0.391
+0.000
1319
1234
-85
1.07
WLAlign
0.328
0,322
-0,006
4018
1276
-2742
3.15
SLOTAlign
0,200
0,200
-0.001
891
821
-70
1.09
HOT
0.173
0.172
-0.001
239
226
-13
1.06
JOENA
0.583
0.583
+0.000
691
679
-12
1.02
0.8
Official
PLANETALIGN
x1.02
JOENA
x1.07
MAR
x1.24 BRIGHT
NeXtiAlign
0.4
x3.15
WLAlign
16 WAlign
x1.42 CrossMNA
x1.12
NetTrans
0.2
x1.09
x3.43
x1.06 HOT
SLOTAlign
REGAL
0.0
104
10a
10a
Training Runtime (s)
Figure 7: Performance and runtime comparison between official and PLANETALIGN'S implemen- tations. The x-axis shows the average training runtime, and the y-axis shows the average MRR of different NA algorithms across 18 datasets. The average speedup runtime of each method are shown in green.
We conduct comparative experiments between the official and PLANETALIGN'S implementations of NA algorithms to validate the correctness and efficiency of PLANETALIGN. To ensure a fair comparison, only include algorithms that have official Python implementations to eliminate the efficiency discrepancy different programming languages. All training parameters, including training epochs, set default in the official code. We report the average MRR and training
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runtime of official and PLANETALIGN'S implementation across all 18 datasets in PLANETALIGN in Table 5 and Figure? 7
We that PLANETALIGN'S implementation show comparable performance across all baselines while achieving up times speed-up over official implementations, demonstrating the correctness and efficiency of our implementation of existing NA methods.
D.4 SCALABILITY RESULTS ON LARGE GRAPHS
To further demonstrate the efficiency of our implementation on large-scale networks, we compare the training runtime with the official implementation on networks of 50K, 75K, and 100K nodes with an average node degree of 5 per network. As we can see in Table 6, PLANETALIGN consistently outperform official implementations with up to 2.7 times speed-up.
Additionally, we can also see from Table that most existing NA methods cannot scale to million-node networks due their intrinsic complexity constraints, highlighting more efficient NA methods as an important line of future works. PLANETALIGN intentionally preserves the theoretical complexity of existing methods to ensure fair and consistent benchmarking.
Table 6: Runtime (s) comparison with official implementations on large-scale ER networks of 50K, 75K, and 100K nodes with average node degrees of 5, OOM represents out-of-memory.
# Nodes
50K
75K
100K
Version
Official
PLANETALIGN
Specdup
Official
PLANETALIGN
Speedup
Official
PLANETALIGN
Speedup
REGAL
334
214
1.56
583
390
1.49
1.12x10*
731
1.53
CrossNA
4.12x10
3,02x10
7.36x10*
5.13x10
1.37x10
891x10
WAlign
1665x10
1.29x10"
1,28
3.12x10"
2.54x10*
1.23
OOM
OOM
OOM
BRIGHT
2.82x104
211xx104
1.31
8.9xx
6.90x104
1.29
3.47x10
2.49x10
NeXtAlign
6.55x10*
6.08x10*
1.08
3.68x10'
3,41x10*
1.08
OOM
OOM
OOM
WL WLAlign
2,46x109
9.12x10*
2,70
0OM
0OM
OOM
0OM
OOM
SLOTAlign
3.0
2.75x100
1.10
0OM
DOM
OOM
OOM
0OM
OOM
HOT
3,14x10
188x10"
1,10
6.17x10
5.74x10²
1,07
1.08x10'
9,43x10*
1.15
JOENA
2.31x110
2.28x10
1.01
8.30x
8.12x100
1,02
OOM
0OM
OOM
D.S DETAILED EFFECTIVENESS RESULTS
Detailed effectiveness results on plain networks with training ratio of 20% are shown in Table 7 and 8. Detailed effectiveness results on attributed networks with training ratio of 20% are shown in Table 9.
E STATEMENT OF LLM USAGE
In this paper, LLMs were used exclusively for formatting assistance and language polishing. At no point were LLMs involved significantly in research ideation and/or writing to the extent that they could be considered as contributor. Therefore, the of LLMs does not impact the methodology, the scientific rigorousness, or the originality of this research.
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Published as a conference paper at ICLR 2026
Table 7: Detailed effectiveness results (Part of II) on plain networks with a training ratio of 20% The Is//2nd/3rd best results are marked in red/blue/green respectively. Time denotes the inference time and Mem. denotes the peak memory usage.
Dataset
Foursquare-Twiite
Phoue- Emnail
Metrics
1i
MRR
Time's)
Mem.
Hiss1
1üts 10
MRR
Timets)
Mem./GB)
hoRRan
1.87
0645 2.00
1431
2156.1 2000
11000
a.25
0,5922aa
PINAL
104741.0
2407 L.00
1.02
0,96
2.30
2725-1300
3000
1257
0.10
0.63.2a1
JONB
0451 ao
L10kwex10
1.20
A057
1952
2.
0.00
REGAL
.0001 1.000
0027
0025
1.10
10122-1000
0097
2011
1077.11000
1,30
ClosMIN
1011 mm
0456+ 300
L10toex
100
A805=100
2163+ 306
NerTrans
24201 0000:1:000
1347-100
5.90 102
3.50.2129
1016
2546:1 3/01
1020:2/01e
2 10
0.85
WAlign
0000-1:0m
0150 van
2.30
M008 2010
1255+ 395
0 000r
0.89
BRIGHT
117842.0012
09231 300
9,30ia
.476
2516:1
.18862.002
0.29.oozt
2.95
NeXtAliga
1420+
0745 005
7.90 20"
a570=101s
3012
4 601-001 1411=/ck
0 0.86et
WL WLAlign
0924
2103:
1325
1.301arex 10
300
1764
265911
1412:1/014
7,50.2 LOP
1:00
PARREIT
1203
2006-
1776 1m
1.0tamex10 16
2600
208T=100
7531 300
4374-100
0.76-tca
0
SLOTAlign
1172.
06/4...0
1.801aex 102
2.50.
1075
062511 x0)
1103
2,362003
0 1.55
HOT
0457+
170
0775-1015
9273+
JOENA
2,901aee
2,50i
2.76.
0.72.ta1
Dataset
ACM-DBLL P
SarchCernl SarchCure2
Metries
1isee
Hite0 10
MRR
Tiue(s)
Men.(Gi)
Hiss01
Hils 10
MRR
Timets)
Mem.(GB)
IsoRank
1641
A329 1044
3023 MG
4,701110000 10
4.30.1o0
1035
2254.1 2016
1976.1100
0.95 Ln3z
1.50..
FINAL
2562::0000
6893
3612 900
3,90
4467
2579 3000
1063=e/0x
0.Seimnca
1. 1,70
JONE
251551.
7267 M05
3479.1
1.30aaex 10
1458
21001
385
099211c0e
8 10P
1.30
REGAL
0357
1367
1. 10
190
1023:110
0087
1.20
ClossNA
.0742.2.004
2250:2.02
5.50 202
190iio
1046620000
11522
0492:/023
NarTram
5430-+amm
0523-1a1
2.20
WAligs
3797+-001
0207-1006
01366-/214
1.00mxan
BRIGHT
6.10
2188
NeXtAlign
3915
L.00bawx
20734:
863622/04
1.50504
1:30
WT Wl.Align
3152
5446+ 008
4183+
1,40hixx 10
7000
0535-m01
1639+ 3v
0638+ion
5 8.80 10
1.50
PARROT
5749-1
5794
1.00mwxx00 102
70:
3720 3:01
5.0
1.00
SLOTAlign
4914
7174
9,90 102
1000
000311 2001
0028
1.00
2. 10:20.0
HOT
3261 +a0m
6787 o0
2210+t-000 mns
4,50aasx 10
500
1998+ 309
x
1.50ez
JOENA
6149
3062
7136 may
3,50 10
300
0689
22941
382
1120 Lien
Datasel
DBPI5K ZH-EN
Italy I-Ially
Metries
H
11
MRR
Time's)
(eml(Ga)
Hits
Hit
MRR
Timets)
Mem.GBB
IoRRank
109222.001
2276:2:000
140barx 1P
10
8424-100
2176:
3009
1054:2000
0000
FINAL.
+307
uxe
2634+ 800
1.0h
0430-rae
0058
JONE
125
L50
8.70
3149
REGAL
0068.1.000
0091 1.00
3,00 10
1013
1025811
2011
01:4220023
0.7
0.81 2i:.0
ClosMINA
2816 161 095
1077+-00 0017
9,00 16
0023-=0015
1447+ 3155
0476mm0e
9.70-00xx000 10'
0.98-taa
NetTrans
2625 1:101
5023 2013
3717.1 k.y
5.001aza 102
3,00 L0.
1603-1015
2248.1 3017
1110.1c
1. 1
WAligs
1856:2.00
2823
2231 127
9,10
1S0suuuxI1 IIn
06099220114
1550:2 3044
U U.M5SnED
1. 1.
BRIGHT
27:5+
9936+
3789+
1.20tawx10 10?
10-tooxx10 10'
0604- 101s
2566-
1445
0.33tora
1000=
NeNtAlign
2.05
5981
2.601aex 10
10..mm1111 10
2580.1
0.31
1:20
WLAlign
2540
2911
2.90baax 10"
84044
1136:t
2 A0l
1.40
PARROT
63341.000
8524
4.70aanx100 102
10
0993
2848.1
1655
1.00
L.
SLOTAlign
3725
0581
3,06baa0x 104
0049
06133
0.125n03
HOT
2000
2.10 202
200
0639
2408 3006
2:10::
JOFNA
6.70tuexx 10
20
Datasel
Doaban
Vlick-LLadFM
Metries
Hiss@1
Bits8 10
MRR
Time's)
Mem,
1ltt@1
litt@ 10
MRR
Timets)
Mem./Bi)
BoRRan
5179
2517 v01
0.50-au
0.95tuua
0028-raae
0815 men
9 9:20-000x0000 10
&13
FINAL
.676c
2692
0,34
1.14
0028
1.29 10'
JONE
2777.1.84
53
3936
9.08 1.0
0.39
1013
0437
0239
4.09 100
REGAL
8025
0198:
0105
2.57
0042
05400:
1.782nsscco
CrosMNA
0187:t.0m
NetTrans
2000012000
3291 2018
1.922n43
0220411 2015
27752200110
WAligs
1450)--001
3001
02200=1014
DRIGUT
1200.1.a0r
A361 mm1
2218
2.6
02.99 1019
0692.1 3007
0057
A 10
NeXtAlign
2154:1.00
5700 2644
3305-2.04
1.66ioo
02200-100
00411 3055
(137522023
6.1115x22x111
W1 Align
2008+
3505+ 00
2517+ mm
6.58 Sa- 102
0.0
0080 car
0224+
307
0144 1111
5.37+5555i 10
PARROT
5832220000 2000
400
2.78
1.31
0000
3000
2.1
2.51 101
SI DTAlign
-000+-00
0676+ oxe
0048++
7.01 tass
100
0041-rae
0235+ xei
0015-roox
1.54-eaxx
101
HOT
1509 Line
4600
1.45
3.98 10"
137
0091
015711000 300
1063
1.01 1012N00
7.97
JOENA
8091
3657
1,41
0090
099411
0558
2.0
1.4 16
Dataset
Flicke-MySpace
Avenas
Metries
1iss
Hits@100
MRR
Time's)
(Gal
Hitse1
Hiss0
MRR
Timess)
Mem.GGB)
hoRonk
0047:1.00
0235
2,00
3
3981 1981-1ae
6751.1300
0.09 2013
1.02
FINAL.
0000
0093 oxe
0048 ma
6.74
5:1 Hoore
3849-196 ree
8811 x00
5503
1.3
JONB
0056
0266: 198
19 10
9130
9783 3M0
9103:110M
1399 10*
REGAL
0061
0123 1927
0173+-w07
9801 xe5
14
CrossNA
.107 MC1
008011.00
2,90 1
4261
8652.1 1130
.8122.114
1.00
NetTrans
0000
.0091 2013
0010
738
8827
99531 3004
.348
L.7
2.23
WAllga
1056
0444 2015
0240
2.4
9923 2000
997448 xe3
9637:2/0
0.
1.34
BRIGHT
0007
0154 mg
2.06 traox 10
5.
0700 m00
9954 305
961 7::
0 0.3e
1. 1.20
NeXtAlign
0047:2.117
01 002
00891 a04
1.66aaex 20
201inz
.347
7259 301
0.282
WL WLAligo
1000::00000
0070s
0943 001
5.67 100
1.1enio
4051 1803
7152 300
6401=100
6 6477m00010
0.89
PARREIT
0003-+-am
01644
0114+ am
H.7 110
6.0etors
9780-1e6
0939 3:0
9586-100
0.88-0r5
1100
SL. SLOTAligo
0321 +-100
.013+
6075+ amn
9,78
6.31
9785-106
09330 3001
0580-100
0 0.12mmm
HOT
0014
.0:33.
4,661144110 a.
102
4.37.
9653
9958.L
.50
1
81911.6
JOENA
0071 11:000
(4776
0261 1
4,49
179
9995-100
9939
0596
2222
10
27

---

Published as a conference paper at ICLR 2026
Table 8: Detailed effectiveness results (Part II of II) on plain networks with training ratio of 20% The Is//2nd3rrd best results are marked in red/blue/green respectively. Time denotes the inference time and Mem. denotes the peak memory usage.
Dataset
Cora
ArXiv
Metries
11te@1
10
MRR
Timeis)
MemaGGii
1ltt@11
1ito@ 10
MRR
Timeisi
Mem./GB
IoRRank
1877
3127
1.98
1:17ians
2247
155771 3001
1.42 10
FINAL
2065:2:0000
0.25
1.38tuu
7250: 3000
410221100
161
2.22
JONB
3611
L.Haawx
.195
166
8.79
REGAL
0155-:0000
0583
2.4
/0026
01
0098=/006
ClosMNA
A574 mwa
1740:2 662
5,80bamx00 30"
2015
667244 3027
2.87 4015:20012
NerTrams
3703++am
7236- 0017
4891 +
1888 hars
4359 1019
7831+ 3000
14 10
8.84 444-r-a
WAlign
.176.1.-44
56501 2044
.47531 w00
0.23 Lawi
1.85 1.8t
2208
26541 3m5
2785 1004
2.52. 10
2. Lan 10
BRIGHT
3520
59664
49344: 063
3.61 Haae
1.8
4216
7263 36.0
52700=.0n
3 3006tmu2x10
1.1 1.13 aox 10
NeXtAlign
4056.2.01
7212
5192:t.047 9:47
1189
7451 3/6
529
2.91 Eoorre 10
78
16
WLAllgn
275412
A3982.00
3349
&.02 202
1.25anes
4873
659311
5425
5.98
2.77 10*
PARROT
6961 1:000
56 30
7509:1
hrus
1.7
7259
9158 3:0
6.0 10P
2.55 sox 10
SLOTAlign
704441..00
1.79aam
1.962
2342
485311
4068
7.01152ix11P
2.5 10
HOT
6413e
2481
3.8
6475
2
1900-00xx000
1.50 10
JOENA
3006
Dataset
PP1
GGI
Metries
Hise
1
MRR
Time's)
Meml(Gal
Hiss01
Hil 10
MRR
Timets)
Mem.GB)
IoRRan
3622
4175 100
4462 100
2.19
1.3
25122000
50642 3000
337722000
3.5
5,33
FINAL.
8191+
5743+
1500tur
1920-rase
6306 3m0
3402 100
4 4ol
7.42
IONE
5218:
L68 20
1.37
m02
6829 3099
1652=100M
1.72mnss<
3.32
REGAL
01:00:
0034
0330-+
K tar
0.94tum
0171-
0780: sxx
0187
16
2.52
CrossMN
8045.1.00
91791
8445
Asaiz 20?
1.09
695411
1.8
2.67
66591110
670811
WAlign
3791:1
BRIGHT
.482811.00
5357
2.37as
1900
557001 3003
.641-2100
AigiAlign
4576
6098 0014
5248+-a00
4.65 tren
2809-1000
5263+ 305
3777-110
WLAligh
81261
7682.1.
1.04Lanxx00
1.74
5640.1 2012
.4710_.00
1.82 10"
9.40
PARROT
9926..0000
9731
L80Laww110
1.58
1203
937331
8621 L1o0
2.30 1PP
SL SLOTAlign
7508:2.000
8219 100
76844 100
3,98-ba2
7170=000
8097 30.7
7500b=2211
1.98:oosx11f
R. &15eras
HOT
3822
4667 wes
2052:t.a41
5.66bamx 10
700
3466 100
4724 wes
1901
8.67 10
JOENA
3995
9907 1
0,542
1.0
8665-100
9553 3011
9016:2000
1.01 101100110
1992
Datasel
DBPISK JA-EN
DBP15K FR-EN
Metries
Hisee
MRR
Time(s)
Mona(GR)
MRR
Timets)
Mem./G))
TeoRank
+285+
1,51 +ownoot
1072-1aw
5034+ se1
2381-
1.500ussc100
1.90 1o
PINAL
5580 M00
2784
L80 Lavex 10
2 Looss10
1414__0
5701 300
2781 2781 Looe
2.45 10
IONE
0405
0003+
1S5tuux
0166-1as
1119+ 395
14
REGAL
0153 M82
0250.L.a07
3,06 Lawax
Lott
1065__iae
0247 2x07
0140.1100
3.16 10'
5.92.am
ClossMN
2932 2912
1000-:000 M00
ba0xx000000 30
2858:2 300
1121
1332500ss110
6.23
NetTrans
3044
A373
41081 800
29662amx 202
2975:1100
6457 3007
2.982022110
1443 10
WAligs
2334
3207
2673+
1.46ha1
125ttoo11 10'
1638-rem
2432 3m7
1046-109
116ttrse10 10"
600e aox 10
BRIGUTT
3264
8255
4267
3.91 19L1asxx10 102
1.P.ioms110 10
1143
631311
4203
1191 16
NeXtAliga
2.66basex 20
1.A3iuux 10'
2015
5951
2 A0l
1.34 10"
WTAliin
663-oo00100 10"
2.90 10)
PARROT
71648
9.12 1022
50586
2.NF 10
DiTAlign
0063
0094+
0157+
2.174amx
10
0188-1ae
0725
0081
1.1-torexe 10F
2
tol
HOT
1911n01110
3X1
207ss11x000 2010
1.02110x
JOENA
6,42619x
2667m0x10
7133
200
6.7200x111
2.67 10
Datasel
Airport
PeMSus
Metries
Hisst1
Hit- 10
MRR
Timeta)
Mrm.GGi
Hit@81
Hite6 10
MRR
Timeia)
Mem.
hookmk
1308 Low0
3755
2074....
0.32
1537-100
735311 xo)
4197:110x
0.23
0.55
FINAL
21:7
6318
3530
0.92:001
1985-1es
7574 so)
3017:108
000
0.64
JONE
1.09
2073
5575.
1661anx00 10*
103100
3824
672811 2311
4533
743
0.842
REGAL
1477
0698
L17hue
0.3
0493-1019
2272 3156
1667:116
0.8
CrossIA
.4304...441
1
0.98inm
10665-1044
064011
0.10.1003
1.9
NarTram
4203-
5154+
1777
3085
8704
0.77-041
WAligs
22700-23112
0.13
8441
67
0.13
1.162t
BRIGHT
3405
t,47t
NeXtAlign
2046-
WLAlign
.4761 1io0es
5107
5252.1
7,06
1.33inea
1676
312511 3012
1.28
PARROTT
9228
SLOTAlign
10000
2813
9,89
1.12.in23
9118
194311
1.5ixus
HUT
2580 2-024
4645-t
1737
1,54euux
2:17
6103=0005
8566:t 2005
0.45m
2:00aa
JOENA
3637
8887
0,07:bum
1.03
5890
39441:2 3000
9568
1.042100
28

---

Published as a conference paper at ICLR 2026
Table 9: Detailed effectiveness results on attributed networks with a training ratio of 20%. The 1sd/2dd3rd best results are marked in red/blue/green respectively. Time denotes the inference time and Mem. denotes the peak memory usage.
Dataset
Douban
Flickr-d aM
Metries
H
H
MRR
Timels)
(enl
Hits01
Hil 10
MRR
Timets)
Mem.(GB)
FINAL
96.38
2013
6959:1.am
0,90
0.87
0152:110e
1022:1 3004
0422:110x
3,00
REGAL
1525 2012
0758.1
3.20
2.55 Lous
0086
0560.1 3006
1283
263 LonzN 10
2.70
NetTrans
32742
5145
4236:2 904
1.23
100
1401 2012
3 X10
2.4 10
WAllan
2855
3798:2 wx3
1.25
0169:2000
107 10702 302
0416:2000
2 2.30.anss
4.6 4.62.1a5
BRIGHT
28:3+
4095+
3966-t-0e
1.52+ac
145thrs
0145-101
1019+ 309
1902-109
3 1,00
NeMtAlig
1879
1918
2c5
1
1083
088411
2.0
2.00
PARROIT
6413+
3408-
7481 + mm
4.25+am
0442-100 ra0
1064 300
0700 1100
1 10'
SLOTAlign
4397..
7207
L.ve
1.31 Lott
1065
0387
300
0205
2.88
Los 10
HUT
4391
8.76ba21
0152-100
0525
01141
JOENA
7535+
0600
Dataset
Hlicke-MSSpace
Areuas
Metries
Ilse1
1
MRR
Timels)
Mem.tGil
His@1
lite 10
MRR
Timets)
Mem.GB)
FINAL
0234
01132
2.33
128
900911 2000
1928
06123244
0.882101
REGAL
0155 me
145taus 10
8061 066--100 100
9815t am
9181 11:7
202
1. 1.32tess
NetTrans
0075
8074
1.32in00
987912
2035
9688
100
1.17
WAllgn
0112.1.00
0508 2914
1961
2.6
9808
9978
300
177811
2. 119
BRIGHT
4832 40001
0156:t-002
3,50baiex 20"
2,38
977444006
9950 365
9663-1/0M
0.51
1 1015:
NeXtAlign
01622:005
0,91 deam
1684
X230
7344
0.475003
PARREIT
02234 10
1 +amex 26"
06 1677
0999
3000
103
2.5
0.77
SU. SLOTAlign
0257
0163:1
1.0 10
0891
0939
9042:10n
0.88
HOT
0000-1.0000
00572.000
00241
3.26
1009
4.20
1.15
JOENA
0117.1001
268411000
0545.1.90
3873
9999
9929
0.34
1.02
Datasrt
ACM-DBLP
Cora
Metries
Hise
HitsiP
MRR
Time(s)
Men.(G))
Hits091
Hilv0 10
MRR
Timets)
Mem.(GB)
FINAL
4054+1000
7080 m00
5366---00 900
1.19
7891-
9026- s0n
8532-100
188
REGAL
.4159
A373 MMA
4890.1 304
1 188 30
1.78
A376
4706
2271
5.15
6.84
NerTram 687446/m
9300 2014
7716-t-00
2.6 102
48
7007=441 4017
8005 us5
7055em.41 413
147
1.32
WAlign
6675
9109 2012
752411 m00
88 Lous
9551 1012
9724
300
9621 1ue0
3338
2 2,36
BRIGHT
S5700
6163 M01
L.22219x 262
2.35an0
7989
1902
88132/028
6.072369
1.32
Aelig
3512+-00
7633+
4851+ 0111
145tharx 10
3336-14m c0r
A620
4444-
1.56
1. 1.Seean
PARROT
6867 20000
3437
7770:2 8000
L.0 262
1.27
96542
3000
3667
1. 1.13axds
SI DTAlign
6633+
6730+
7400++ 000
H.34 10
9949-raa
0930
9074-110
1.92+x19
2,0++a
HOT
3893
S1801
235
4.28 9.78
2.85 Lott
1493
7549
L162
1.29
JOENA
3647
3568
9947
9999
1.14mc
Dataset
PP1
DBP15K. PR-EN
Metries
Hise
Hiss 10
MRR
Time(s)
Mrm.iGR)
Hise1
Hitee 10
MRR
Timeis)
Mem.(Gi)
FINAL
1564 oxe
0760:1.00 am)
1.87
0.90
1954-10s
4381 301
2500:110x
4. 4.36 10
2x 10*
REGAL
11555
2910 2010s.00 o012
2012 005
9,30baaa
3
0007
00158 3004
1005et.0oM
9.33-maas 10
NerTrans
7356
8581 15 wM4
7783:1 wcT
base 10
A308
7788
3:11
541 2:2
1 133 10
1.00 1.03traax x 101
WAlles
7667/2.021
5733 awo
80411 000
6.8421544
3.15inot
3077:20044
669212 3M4
2.03 55722.40
1.67 10
BRIGHT
7156t-000
9623+
7691
4.76
129
A393-1014
8020-+ 301
5652-1eN
2. 488hhess111 10
9.55-era
MrXtiii
0306:2.00
1009
bas 20"
1:25enn
4781
37700
6109
2.78
9.87maaa
PARROT
9916.1.001
9943...0
3.58 20
1.58
.037 1035
9550.1
2. 2.76 10
OTAligg
9618
3.70eani
1.71
7619
8721
1.700000x110
2. 28 10"
HOT
3.64
6172-1044
7960:1
JOENA
0.73axu2
Datasel
Airport
PeMSo8
Metries
1
Hitei 10
MRR
Timeess
Mon.GG))
Hiss09
Hits0 10
MRR
Timeis)
Mem.(G))
FINAL
2207
3:141.00
0,47
0.39
.2132_1000
2610.1 2000
.051 11o0
0.02
0.90
REGAL
0516
2096 sess
1063-t-a am
3617 cem
5655-t 3500
0.22
1:20
NerTrams 2430-+-w0
4667+ o11
3192++aa0 am
6353 cose
8963+ sox
71127-iet 1eth
007
1. 1.18
WAligs
3041 tr
5803- mes
3987-+-am am
1,50+as
2.58ebora
4063 2010
0056-+ s/s
7680
2.15
2. 2.20
BRIGHTT
5671 MKA
La2e Lase
1006
.280 1000
.766
3197
0
1.06in
NeXtAlign
0792:1.051
25512.130
1.
0,46
1022
2092 1897
7162.2 1285
0.022004
0.99
PARROT
8582.1.00
9622 2000
8971.1.a0
2.28 Linnx 20
0.92.
8787 100
9596 3:01
9071 Looc
8311
L.
SLDTAlign
8918:1/00
36 1.6
9256.1.am
0.15mmm
b.98
9999 3, 3/01
9926
0.0
0.902
HOT
4318
5770
241332
5.33
1.36iinu
7324
388332 1130
3921
2.35
1.162100
JOENA
38724
9117:1.000
0,10
1.02.nn0
59999-2006
399991
2000
3999
0.052nt3
1.02
29


Reviews:
[{"review_id": "qwXgyAbAPZ", "reviewer": "Reviewer_2QXK", "summary": "PlanetAlign collects and organizes under clean and standardized APIs, datasets, methods and benchmarks for the network alignment problem: 18 datasets, 14 methods, standard evaluation metrics (Hits@k, Mean Reciprocal Rank (MRR)),  diverse benchmarking settings,  emphasizing effectiveness (e.g. supervision regimes), scalability (e.g. logging), robustness (e.g. controlled noise injection). PlanetAlign is put into a comprehensive test: empirically evaluating all methods over collected datasets (semi-supervised setting), reporting on metrics and drawing insights on what type of methods work better than others or their particular requirements).", "questions": "- Are there provisions/facilities for the user community to easily \"extend\" PlanetAlign (i.e. integrated methods/datasets into it) - rather than the authors expanding it as in Appendix E?\n- Any thoughts for a kind of leaderboard for the network alignment community?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- This is an excellently organized manuscript, describing the design and practical usage of a solid, software engineering work.\n\n- The engineering contribution is substantial and noteworthy: links to related, mature artifacts (code repository, documentation) are conveniently provided. Abstractions identified are well thought-of and disciplined. \n\n- It includes reports on extensive empirical studies: it would be hard to imagine comparing so \"easily\", so many and diverse methods for network alignment over multiple (and multiple-type) networks prior to this work. PlanetAlign makes a Herculean task, manageable: it certainly accelerates \"network alignment\" research. Figure 3 provides a compelling summary of the development simplicity made possible.\n\n- Nicely reuses and integrates organization ideas and abstractions from other, highly-popular, open-source projects (e.g. pytorch-geometric).\n\n- Impressive performance comparison agreement of PlanetAlign and \"official\" implementations of methods.", "weaknesses": "Relevance to ICLR audience is the major weakness of this effort:\n- PlanetAlign is an (excellently engineered) collection of existing artifacts for network alignment methods, so  - although by \"design\" - the novelty dimension is limited, in particular for ML audience expecting emphasis on deep/representation learning (although there is an embeddings-related component part).\n- This work would be potentially a best fit for a venue focusing on networks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MRFxNrvSIK", "reviewer": "Reviewer_CB1K", "summary": "This paper introduces PLANETALIGN, an open-source Python library aimed at providing a standardized platform for evaluating and developing network alignment (NA) methods. Network alignment is essential for identifying node correspondences across multiple networks, a task central to applications in various domains, including social network analysis, bioinformatics, and knowledge graph fusion. Despite the importance of this task, there has been a lack of a comprehensive benchmarking tool for NA methods. PLANETALIGN addresses this gap by providing a unified library that integrates 18 datasets from 6 domains, supports 14 NA methods, and offers a rich set of evaluation metrics. The library features easy-to-use APIs and a flexible design, allowing users to benchmark and develop new methods for network alignment. The authors claim that their library outperforms previous solutions in terms of speed and provides in-depth comparative studies that highlight the strengths and limitations of existing NA methods.", "questions": "1. While PLANETALIGN supports a few OT-based methods, could you elaborate on potential future plans to incorporate more OT methods, especially those that handle non-convex optimizations better?\n\n2. How do the methods in PLANETALIGN perform when applied to networks with millions of nodes and edges? Have you tested the library's performance on very large-scale graphs?\n\n3. Could you discuss potential plans for updating the library, such as adding new NA methods, datasets, or advanced evaluation techniques in future releases?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper offers a large collection of 18 datasets from diverse domains and 14 NA methods, providing a solid foundation for comparison across different scenarios. \n\n2. PLANETALIGN emphasizes reproducibility by using standardized evaluation metrics (Hits@K, MRR) and consistent dataset splits. The inclusion of time and memory usage metrics makes it easier to assess the scalability and efficiency of algorithms.\n\n3. PLANETALIGN has demonstrated significant improvements in execution time, with some methods being up to 3 times faster than their official counterparts.", "weaknesses": "1. Many datasets are synthetically generated or permuted from a single network, which may not fully capture the heterogeneity and noise of real-world multi-network environments.\n\n2. The evaluation focuses heavily on quantitative metrics, but does not address the interpretability or explainability of the alignment results—a critical aspect in domains like bioinformatics or fraud detection.\n\n3. The paper emphasizes the library’s ease of use and API design but provides no user study or external feedback to validate these claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cwfmGscqD0", "reviewer": "Reviewer_rJCh", "summary": "This paper introduces PLANETALIGN, a comprehensive Python library for benchmarking Network Alignment (NA) algorithms across multiple domains. The framework integrates 18 datasets spanning six application areas (social, biological, academic, knowledge graph, infrastructure, and communication networks) and implements 14 representative algorithms, including consistency-based, embedding-based, and optimal transport (OT)-based methods. PLANETALIGN provides a unified PyTorch-style API, standardized evaluation metrics, and extensive experiments assessing algorithms on four dimensions—effectiveness, scalability, robustness, and supervision sensitivity. The work fills an important gap in the NA community by establishing a reproducible, standardized evaluation foundation, though it introduces no new alignment algorithm or theoretical framework.", "questions": "(1) How does PLANETALIGN ensure fairness when comparing algorithms that rely on different supervision levels (e.g., supervised vs. semi-supervised vs. unsupervised settings)? Are hyperparameter budgets standardized across methods?\n(2) The paper claims scalability and efficiency advantages. Could the authors include additional results on large-scale graphs (e.g., >1M nodes) to substantiate this claim?\n(3) The framework currently focuses on pairwise alignment. Are there plans or design provisions to extend it toward multi-network or temporal (dynamic) alignment scenarios?\n(4) Since the results show that OT-based methods consistently outperform others, can the authors provide more theoretical intuition or ablation evidence to explain why OT-based models are so robust across domains?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "(1) Comprehensive benchmark coverage: PLANETALIGN integrates 18 datasets from six domains and 14 representative algorithms, providing the most complete evaluation platform to date for network alignment research.\n(2) Systematic multi-dimensional evaluation: The framework assesses algorithms along four complementary dimensions—effectiveness, scalability, robustness, and supervision sensitivity—offering a more holistic perspective than prior work.\n(3) High implementation quality and reproducibility: The library provides a clean, modular PyTorch-style API, reproducible experiment settings, and optimized code achieving up to 3× faster runtime compared to original implementations.\n(4) Insightful empirical analysis: Experiments yield practical insights, such as the superior stability of OT-based methods and the efficiency-memory trade-offs in embedding-based approaches.\n(5) Community impact: By releasing a well-documented, open-source library, the work provides an infrastructure that can serve as a long-term standard for the NA community.", "weaknesses": "(1) Limited algorithmic novelty: The paper does not introduce new alignment methods or theoretical developments. Its contribution is primarily infrastructural, which may limit interest for readers expecting methodological innovation.\n(2) Incomplete scalability validation: Experiments are limited to graphs with fewer than 100k nodes. Given the scalability claims, evaluation on larger-scale graphs (e.g., >1M nodes) would better support the authors’ argument.\n(3) Shallow theoretical motivation: The paper lacks deeper theoretical analysis or justification for why OT-based approaches perform better, relying mainly on empirical evidence.\n(4) Restricted scope: The current implementation only supports pairwise alignment. Extending the framework to multi-network or dynamic alignment scenarios could significantly broaden its applicability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jh8CDCNeL3", "reviewer": "Reviewer_67gk", "summary": "This paper introduces a new python library, PLANETALIGN, for the evaluation of network alignment algorithms. This new framework includes 18 datasets from various domains, like social, biological, knowledge graph, and publication. It supports 14 methods which covers major categories of NA algorithms. \nThis new framework provides unified APIs, and standardized evaluation metrics for model implementation and evaluation. The author conducted experiments to compare the performance of 14 algorithms across all datasets on effectiveness, efficiency, robustness and sensitivity. \nCompare with the existing algorithms, the new framework achieves similar accuracy with 3x faster on efficiency.", "questions": "My questions are mainly related to the limitations I mentioned above.\n1. The paper mentioned that the multi-network alignment is not explored in this study. I was wondering, with the current architecture, how difficult would it be to extend PLANETALIGN to multi-graph or cross domain alignment? \n2. Are there future plans to extend the support of new datasets or new graph modalities, like heterogeneous graph?\n3. If future versions is improved to support new dataset, especially larger datasets, will it include distributed computation or other scalability improvements to handle large graphs?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This new python library integrates a wide range of datasets and algorithms. It provides a useful and fair benchmark for NA researches. It provides a consistent API and unified evaluation pipeline, which makes the method comparisons easier, more consistent and more reliable. \nThe implementation is efficient and well optimized. The runtime is significantly less the the original baseline. \nThe experiments shows meaningful empirical findings.  \nThe documentation is clear and easy to follow. Users can ramp up quickly with the tutorial and detailed references.", "weaknesses": "The main limitation is lack of extensibility on dataset, functions and customized use cases. This library is mainly focus on algorithm comparisons rather than support the downstream researches. \nThrough this python library is well designed and easy to use, it is not very clear how will it keep up with the fast iterating NA research field. New datasets and new algorithms emerge quickly. \nCurrently this framework mainly serves as a benchmark tool with fixed datasets. The paper does not mention whether there is a maintenance plan to expand to new datasets, or support for customized datasets. \nThis work can be stronger and makes larger impact if the library could be more extensible and more research orientated, supporting NA related researches rather than simply method comparison. For example, add APIs for downstream tasks, customized loss functions, multitask learning, etc. Such improvements will certainly contribute more to the acceleration of new scientific discoveries.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)