# Towards Graph Contrastive Learning: A Survey and Beyond

arXiv: https://arxiv.org/abs/2405.11868 · HTML: https://mlsanity.com/paper/2405.11868/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 125 of its 194 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 30 of 125)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)

## Built on by (most cited first, 26 of 26)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry](https://mlsanity.com/paper/2502.03251/) (2025)
- [Cluster-guided Contrastive Class-imbalanced Graph Classification](https://mlsanity.com/paper/2412.12984/) (2024)
- [InfoNCE is a Free Lunch for Semantically guided Graph Contrastive Learning](https://mlsanity.com/paper/2505.06282/) (2025)
- [Graph Positional Autoencoders as Self-supervised Learners](https://mlsanity.com/paper/2505.23345/) (2025)
- [Str-GCL: Structural Commonsense Driven Graph Contrastive Learning](https://mlsanity.com/paper/2507.07141/) (2025)
- [DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation](https://mlsanity.com/paper/2412.15005/) (2024)
- [DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining](https://mlsanity.com/paper/2502.08949/) (2025)
- [FRASE: Structured Representations for Generalizable SPARQL Query Generation](https://mlsanity.com/paper/2503.22144/) (2025)
- [Towards Graph Prompt Learning: A Survey and Beyond](https://mlsanity.com/paper/2408.14520/) (2024)
- [Enhancing Frame Detection with Retrieval Augmented Generation](https://mlsanity.com/paper/2502.12210/) (2025)
- [HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation](https://mlsanity.com/paper/2505.19020/) (2025)
- [Toward General Digraph Contrastive Learning: A Dual Spatial Perspective](https://mlsanity.com/paper/2510.16311/) (2025)
- [Learning Fair Graph Representations with Multi-view Information Bottleneck](https://mlsanity.com/paper/2510.25096/) (2025)
- [A Generative Graph Contrastive Learning Model with Global Signal](https://mlsanity.com/paper/2504.18148/) (2025)
- [Fractal Graph Contrastive Learning](https://mlsanity.com/paper/2505.11356/) (2025)
- [Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning](https://mlsanity.com/paper/2505.23529/) (2025)
- [Metapath-based Hyperbolic Contrastive Learning for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2506.16754/) (2025)
- [Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers](https://mlsanity.com/paper/2510.22555/) (2025)
- [Understanding InfoNCE: Transition Probability Matrix Induced Feature Clustering](https://mlsanity.com/paper/2511.12180/) (2025)
- [HTG-GCL: Leveraging Hierarchical Topological Granularity from Cellular Complexes for Graph Contrastive Learning](https://mlsanity.com/paper/2512.02073/) (2025)
- [Feedback Control for Multi-Objective Graph Self-Supervision](https://mlsanity.com/paper/2602.05036/) (2026)
- [Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning](https://mlsanity.com/paper/2604.14746/) (2026)
- [Static and Dynamic Graph Alignment Network for Temporal Video Grounding](https://mlsanity.com/paper/2605.00684/) (2026)
- [Timestamp-Aware Spatio-Temporal Graph Contrastive Learning for Network Intrusion Detection](https://mlsanity.com/paper/2606.17109/) (2026)
- [α-Graph: Attention-Infused Normalizing Flow Approach to Tractable Graph Modeling](https://mlsanity.com/paper/2609.07961/) (2026)

## Abstract (arXiv)

In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive nature. To address this challenge, self-supervised learning (SSL) on graphs has gained increasing attention and has made significant progress. SSL enables machine learning models to produce informative representations from unlabeled graph data, reducing the reliance on expensive labeled data. While SSL on graphs has witnessed widespread adoption, one critical component, Graph Contrastive Learning (GCL), has not been thoroughly investigated in the existing literature. Thus, this survey aims to fill this gap by offering a dedicated survey on GCL. We provide a comprehensive overview of the fundamental principles of GCL, including data augmentation strategies, contrastive modes, and contrastive optimization objectives. Furthermore, we explore the extensions of GCL to other aspects of data-efficient graph learning, such as weakly supervised learning, transfer learning, and related scenarios. We also discuss practical applications spanning domains such as drug discovery, genomics analysis, recommender systems, and finally outline the challenges and potential future directions in this field.