GraphCL: Contrastive Self-Supervised Learning of Graph Representations

• URL: http://arxiv.org/abs/2007.08025v1
• Date: Wed, 15 Jul 2020 22:36:53 GMT
• Title: GraphCL: Contrastive Self-Supervised Learning of Graph Representations
• Authors: Hakim Hafidi, Mounir Ghogho, Philippe Ciblat and Ananthram Swami
• Abstract summary: We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. In both transductive and inductive learning setups, we demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks.
• Score: 20.439666392958284
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. GraphCL learns node embeddings by maximizing the similarity between the representations of two randomly perturbed versions of the intrinsic features and link structure of the same node's local subgraph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. In both transductive and inductive learning setups, we demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks.

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