mlsanityData to 14 Oct 2026

PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment

arXiv 2505.21366 ↗First posted 27 May 2025cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 48 of its 109 references that are in the map. First posted on arXiv 27 May 2025.

Builds on

48 of 109 references.

References · 48

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
  3. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 371
  4. 2017struc2vec: Learning Node Representations from Structural Identitycited by 215
  5. 2024DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMscited by 167
  6. 2017Predicting multicellular function through multi-layer tissue networkscited by 138
  7. 2022DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problemscited by 129
  8. 2017Cross-lingual Entity Alignment via Joint Attribute-Preserving Embeddingcited by 127
  9. 2020A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphscited by 120
  10. 2019Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matchingcited by 103
  11. 2020Graph Optimal Transport for Cross-Domain Alignmentcited by 103
  12. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
36 more
  1. 2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 76
  2. 2019Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Networkcited by 68
  3. 2021Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learningcited by 44
  4. 2018REGAL: Representation Learning-based Graph Alignmentcited by 40
  5. 2024Macro Graph Neural Networks for Online Billion-Scale Recommender Systemscited by 40
  6. 2014Layer aggregation and reducibility of multilayer interconnected networkscited by 34
  7. 2023Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoderscited by 32
  8. 2022MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybridcited by 30
  9. 2023Class-Imbalanced Graph Learning without Class Rebalancingcited by 29
  10. 2023Hierarchical Multi-Marginal Optimal Transport for Network Alignmentcited by 24
  11. 2024Entity Alignment with Noisy Annotations from Large Language Modelscited by 22
  12. 2022Low-rank Optimal Transport: Approximation, Statistics and Debiasingcited by 21
  13. 2025Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuningcited by 21
  14. 2024Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphscited by 20
  15. 2023Unsupervised Entity Alignment for Temporal Knowledge Graphscited by 19
  16. 2024Expected Sliced Transport Planscited by 19
  17. 2025Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesicscited by 17
  18. 2023Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transportcited by 15
  19. 2024PyG-SSL: A Graph Self-Supervised Learning Toolkitcited by 14
  20. 2023Reconstructing Graph Diffusion History from a Single Snapshotcited by 13
  21. 2025TUCKET: A Tensor Time Series Data Structure for Efficient and Accurate Factor Analysis over Time Rangescited by 12
  22. 2025ResMoE: Space-efficient Compression of Mixture of Experts LLMs via Residual Restorationcited by 12
  23. 2025Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecastingcited by 12
  24. 2024Fair Anomaly Detection For Imbalanced Groupscited by 10
  25. 2024Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptationcited by 10
  26. 2025Joint Optimal Transport and Embedding for Network Alignmentcited by 10
  27. 2024InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Predictioncited by 8
  28. 2022WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learningcited by 7
  29. 2023Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detectioncited by 7
  30. 2025CATS: Mitigating Correlation Shift for Multivariate Time Series Classificationcited by 7
  31. 2025Graph Data Selection for Domain Adaptation: A Model-Free Approachcited by 7
  32. 2024THeGCN: Temporal Heterophilic Graph Convolutional Networkcited by 6
  33. 2015Characterizing interactions in online social networks during exceptional eventscited by 5
  34. 2017Alignment of dynamic networkscited by 3
  35. 2024Harnessing Multiple Correlated Networks for Exact Community Recoverycited by 3
  36. 2020On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Mannercited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 9.

10 papers.

  1. 2025Harnessing Consistency for Robust Test-Time LLM Ensemblecited by 10
  2. 2026Prune as You Generate: Online Rollout Pruning for Faster and Better RLVRcited by 10
  3. 2026Subspace Alignment for Vision-Language Model Test-time Adaptationcited by 5
  4. 2026Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendationcited by 4
  5. 2026Predict, Don't Iterate: Efficient Adaptive-Length Infilling for Diffusion Language Modelscited by 3
  6. 2026Graph homophily booster: Reimagining the role of discrete features in heterophilic graph learningcited by 1
  7. 2026ReMix: Reinforcement routing for mixtures of LoRAs in LLM finetuningcited by 1
  8. 2026AvAtar: Learning to Align via Active Optimal Transportcited by 1
  9. 2026Scalable Optimal Transport Algorithm for Network Alignmentcited by 0
  10. 2026From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Modelcited by 0