mlsanityData to 14 Oct 2026

Graph Contrastive Invariant Learning from the Causal Perspective

arXiv 2401.12564 ↗First posted 23 Jan 2024cs.LG cs.SIcited by 6

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Abstract (arXiv)

Graph contrastive learning (GCL), learning the node representation by contrasting two augmented graphs in a self-supervised way, has attracted considerable attention. GCL is usually believed to learn the invariant representation. However, does this understanding always hold in practice? In this paper, we first study GCL from the perspective of causality. By analyzing GCL with the structural causal model (SCM), we discover that traditional GCL may not well learn the invariant representations due to the non-causal information contained in the graph. How can we fix it and encourage the current GCL to learn better invariant representations? The SCM offers two requirements and motives us to propose a novel GCL method. Particularly, we introduce the spectral graph augmentation to simulate the intervention upon non-causal factors. Then we design the invariance objective and independence objective to better capture the causal factors. Specifically, (i) the invariance objective encourages the encoder to capture the invariant information contained in causal variables, and (ii) the independence objective aims to reduce the influence of confounders on the causal variables. Experimental results demonstrate the effectiveness of our approach on node classification tasks.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 27 of its 33 references that are in the map. First posted on arXiv 23 Jan 2024.

Builds on

27 of 33 references.

References · 27

  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. 2017Graph Attention Networkscited by 8,923
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  6. 2017Inductive Representation Learning on Large Graphscited by 6,320
  7. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  8. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  9. 2019Invariant Risk Minimizationcited by 1,854
  10. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  11. 2020Graph Contrastive Learning with Augmentationscited by 1,208
  12. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
15 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. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 463
  5. 2021Graph Self-Supervised Learning: A Surveycited by 289
  6. 2020Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networkscited by 260
  7. 2020Causal Intervention for Weakly-Supervised Semantic Segmentationcited by 229
  8. 2020Representation Learning via Invariant Causal Mechanismscited by 199
  9. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  10. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  11. 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 141
  12. 2021Generalizing Graph Neural Networks on Out-Of-Distribution Graphscited by 71
  13. 2022Debiased Graph Neural Networks with Agnostic Label Selection Biascited by 25
  14. 2022Variance Covariance Regularization Enforces Pairwise Independence in Self-Supervised Representationscited by 10
  15. 2022Directed Acyclic Graph Structure Learning from Dynamic Graphscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2025AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learningcited by 1
  2. 2025GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learningcited by 1
  3. 2026Causal Neighbourhood Learning for Invariant Graph Representationscited by 1
  4. 2024Quantum Rationale-Aware Graph Contrastive Learning for Jet Discriminationcited by 0
  5. 2025Importance Ranking in Complex Networks via Influence-aware Causal Node Embeddingcited by 0
  6. 2026Transferable Graph Condensation from the Causal Perspectivecited by 0