mlsanityData to 7 Oct 2026

GSLB: The Graph Structure Learning Benchmark

arXiv 2310.05174 ↗First posted 8 Oct 2023cs.LG cs.AIcited by 49

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Abstract (arXiv)

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://github.com/GSL-Benchmark/GSLB.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). Builds on 38 of its 62 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

38 of 62 references.

References · 38

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  3. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  4. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  5. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  6. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
  7. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
  8. 2019Graph Neural Networks for Social Recommendationcited by 711
  9. 2019Graph Transformer Networkscited by 520
  10. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  11. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  12. 2020Towards Deeper Graph Neural Networkscited by 296
26 more
  1. 2019Adversarial Attacks on Graph Neural Networks via Meta Learningcited by 261
  2. 2022Long Range Graph Benchmarkcited by 258
  3. 2019Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networkscited by 241
  4. 2019Learning Discrete Structures for Graph Neural Networkscited by 228
  5. 2020Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddingscited by 212
  6. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
  7. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 152
  8. 2020Learning to Drop: Robust Graph Neural Network via Topological Denoisingcited by 127
  9. 2020Node Similarity Preserving Graph Convolutional Networkscited by 112
  10. 2022Towards Unsupervised Deep Graph Structure Learningcited by 112
  11. 2019Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labelscited by 111
  12. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
  13. 2019Hierarchical Graph Pooling with Structure Learningcited by 81
  14. 2021Graph Structure Learning with Variational Information Bottleneckcited by 81
  15. 2021Mining Latent Structures for Multimedia Recommendationcited by 79
  16. 2021A Survey on Graph Structure Learning: Progress and Opportunitiescited by 70
  17. 2019Graph-Revised Convolutional Networkcited by 58
  18. 2020ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Predictioncited by 58
  19. 2022Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNNcited by 34
  20. 2022Evidence-aware Fake News Detection with Graph Neural Networkscited by 30
  21. 2021Meta Propagation Networks for Graph Few-shot Semi-supervised Learningcited by 25
  22. 2022Compact Graph Structure Learning via Mutual Information Compressioncited by 20
  23. 2022Self-Supervised Graph Structure Refinement for Graph Neural Networkscited by 20
  24. 2019Relation Structure-Aware Heterogeneous Information Network Embeddingcited by 19
  25. 2022The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detectioncited by 11
  26. 2022GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignmentcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 19; 2025: 20; 2026: 8.

49 papers.

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  1. 2024Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systemscited by 129
  2. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  3. 2025In-depth Analysis of Graph-based RAG in a Unified Frameworkcited by 45
  4. 2025Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignmentcited by 41
  5. 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Modelscited by 36
  6. 2024ProG: A Graph Prompt Learning Benchmarkcited by 29
  7. 2024Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learningcited by 28
  8. 2025G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendationcited by 26
  9. 2024Entity Alignment with Noisy Annotations from Large Language Modelscited by 22
  10. 2024Graph Sparsification via Mixture of Graphscited by 17
  11. 2025EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corporacited by 14
  12. 2025Graph-Augmented Large Language Model Agents: Current Progress and Future Prospectscited by 14
37 more
  1. 2024Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learningcited by 12
  2. 2025A Survey of Graph Transformers: Architectures, Theories and Applicationscited by 12
  3. 2024Rethinking Fair Graph Neural Networks from Re-balancingcited by 11
  4. 2025One Prompt Fits All: Universal Graph Adaptation for Pretrained Modelscited by 11
  5. 2024Neuro-Symbolic Entity Alignment via Variational Inferencecited by 8
  6. 2024GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruningcited by 8
  7. 2025MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectracited by 8
  8. 2023Entropy Causal Graphs for Multivariate Time Series Anomaly Detectioncited by 6
  9. 2024A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizabilitycited by 6
  10. 2025Cooperation of Experts: Fusing Heterogeneous Information with Large Margincited by 6
  11. 2023Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Predictioncited by 5
  12. 2025DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Modelscited by 5
  13. 2025DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learningcited by 5
  14. 2024Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Predictioncited by 4
  15. 2025Uncertainty-Aware Graph Structure Learningcited by 4
  16. 2025From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networkscited by 4
  17. 2024Graph Structure Refinement with Energy-based Contrastive Learningcited by 3
  18. 2024GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extractioncited by 2
  19. 2026<SOG_k>: One LLM Token for Explicit Graph Structural Understandingcited by 2
  20. 2024Motif-driven Subgraph Structure Learning for Graph Classificationcited by 1
  21. 2024Bi-Level Graph Structure Learning for Next POI Recommendationcited by 1
  22. 2025FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphscited by 1
  23. 2025Graffe: Graph Representation Learning via Diffusion Probabilistic Modelscited by 1
  24. 2025Beyond Pairwise Connections: Extracting High-Order Functional Brain Network Structures under Global Constraintscited by 1
  25. 2026Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selectioncited by 1
  26. 2026Multi-Agent Coordination Adaptation via Structure-Guided Orchestrationcited by 1
  27. 2026GSLAD: Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detectioncited by 1
  28. 2024Adversarial Robustness of Link Sign Prediction in Signed Graphscited by 0
  29. 2024Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networkscited by 0
  30. 2024S2DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completioncited by 0
  31. 2025OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Taskscited by 0
  32. 2025Weighted Graph Structure Learning with Attention Denoising for Node Classificationcited by 0
  33. 2025Open-Set Living Need Prediction with Large Language Modelscited by 0
  34. 2026Graph Autoencoder for Process Monitoringcited by 0
  35. 2026Model-to-Data Distillation for Graph Neural Networkscited by 0
  36. 2026Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gaincited by 0
  37. 2026Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networkscited by 0