Contrastive Representation Learning Based on Multiple Node-centered Subgraphs

• URL: http://arxiv.org/abs/2308.16441v1
• Date: Thu, 31 Aug 2023 04:04:09 GMT
• Title: Contrastive Representation Learning Based on Multiple Node-centered Subgraphs
• Authors: Dong Li, Wenjun Wang, Minglai Shao, Chen Zhao
• Abstract summary: A single node intuitively has multiple node-centered subgraphs from the whole graph. We propose a multiple node-centered subgraphs contrastive representation learning method to learn node representation on graphs in a self-supervised way.
• Score: 11.416941835869649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-centered subgraphs from the whole graph (e.g., one person in a social network has multiple social circles based on his different relationships). We study this intuition under the framework of graph contrastive learning, and propose a multiple node-centered subgraphs contrastive representation learning method to learn node representation on graphs in a self-supervised way. Specifically, we carefully design a series of node-centered regional subgraphs of the central node. Then, the mutual information between different subgraphs of the same node is maximized by contrastive loss. Experiments on various real-world datasets and different downstream tasks demonstrate that our model has achieved state-of-the-art results.

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