Methods › General › Self-Supervised Learning › Graph Contrastive Coding

Graph Contrastive Coding

4 papers tagged archive 2025-07-28

Introduced by Jiezhong Qiu et al. in GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Graph Contrastive Coding is a self-supervised graph neural network pre-training framework to capture the universal network topological properties across multiple networks. GCC's pre-training task is designed as subgraph instance discrimination in and across networks and leverages contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations.

Paper Source

Papers archive 2025-07-28

4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
18 Nov 2023 · 1 repository · arXiv:2311.11009
Successive POI Recommendation via Brain-inspired Spatiotemporal Aware Representation
29 Sep 2021 · 0 repositories
Graph Contrastive Learning for Anomaly Detection
17 Aug 2021 · 2 repositories · arXiv:2108.07516
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
17 Jun 2020 · 4 repositories · arXiv:2006.09963

Syntology ran 0 of 2 samples · 2 unverified

Tasks archive 2025-07-28

17 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

Task	Papers
Contrastive Learning	3
Graph Neural Network	2
Anomaly Detection	1
Binary Classification	1
Emotion Recognition	1
Emotion Recognition in Conversation	1
Face Swapping	1
Feature Engineering	1
General Classification	1
General Knowledge	1
Graph Classification	1
Graph Learning	1
Graph Representation Learning	1
Link Prediction	1
Multimodal Emotion Recognition	1
Node Classification	1
Representation Learning	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Self-Supervised Learning Graph Models

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