mlsanityData to 14 Oct 2026

Community-Invariant Graph Contrastive Learning

arXiv 2405.01350 ↗First posted 2 May 2024cs.LG cs.SIcited by 4

Not on the map. Why?

Abstract (arXiv)

Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations. However, mainstream GCL methods often favor randomly disrupting graphs for augmentation, which shows limited generalization and inevitably leads to the corruption of high-level graph information, i.e., the graph community. Moreover, current knowledge-based graph augmentation methods can only focus on either topology or node features, causing the model to lack robustness against various types of noise. To address these limitations, this research investigated the role of the graph community in graph augmentation and figured out its crucial advantage for learnable graph augmentation. Based on our observations, we propose a community-invariant GCL framework to maintain graph community structure during learnable graph augmentation. By maximizing the spectral changes, this framework unifies the constraints of both topology and feature augmentation, enhancing the model's robustness. Empirical evidence on 21 benchmark datasets demonstrates the exclusive merits of our framework. Code is released on Github (https://github.com/ShiyinTan/CI-GCL.git).

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 36 of its 64 references that are in the map. First posted on arXiv 2 May 2024.

Builds on

36 of 64 references.

References · 36

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2018How Powerful are Graph Neural Networks?cited by 4,393
  4. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  6. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  7. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  8. 2018Deep Graph Infomaxcited by 1,106
  9. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  10. 2019Strategies for Pre-training Graph Neural Networkscited by 971
  11. 2020What Makes for Good Views for Contrastive Learning?cited by 846
  12. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
24 more
  1. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
  2. 2020Deep Graph Contrastive Representation Learningcited by 539
  3. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  4. 2021Graph Contrastive Learning Automatedcited by 287
  5. 2017graph2vec: Learning Distributed Representations of Graphscited by 230
  6. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  7. 2018Adversarial Attacks on Node Embeddings via Graph Poisoningcited by 109
  8. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  9. 2021AutoGCL: Automated Graph Contrastive Learning via Learnable View Generatorscited by 73
  10. 2021MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graphcited by 65
  11. 2022Let Invariant Rationale Discovery Inspire Graph Contrastive Learningcited by 57
  12. 2021Local Augmentation for Graph Neural Networkscited by 53
  13. 2022Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNNcited by 45
  14. 2022Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrumcited by 43
  15. 2022Spectral Augmentation for Self-Supervised Learning on Graphscited by 39
  16. 2021Graph Communal Contrastive Learningcited by 34
  17. 2023Adaptive Graph Contrastive Learning for Recommendationcited by 31
  18. 2023Neighbor Contrastive Learning on Learnable Graph Augmentationcited by 25
  19. 2020ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networkscited by 24
  20. 2022Augment with Care: Contrastive Learning for Combinatorial Problemscited by 22
  21. 2021Graph Structural Attack by Perturbing Spectral Distancecited by 16
  22. 2018Testing Graph Clusterability: Algorithms and Lower Boundscited by 9
  23. 2023Graph Contrastive Learning with Generative Adversarial Networkcited by 8
  24. 2023CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Cross-Domain Graph Data Scaling: A Showcase with Diffusion Modelscited by 8
  2. 2025Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networkscited by 0
  3. 2025Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networkscited by 0
  4. 2026Instance Discrimination for Link Predictioncited by 0