Self-Supervised Learning

Graph Contrastive Coding

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

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.

Source: GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

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