Graph Communal Contrastive Learning

Graph representation learning is crucial for many real-world applications (e.g. social relation analysis). A fundamental problem for graph representation learning is how to effectively learn representations without human labeling, which is usually costly and time-consuming. Graph contrastive learning (GCL) addresses this problem by pulling the positive node pairs (or similar nodes) closer while pushing the negative node pairs (or dissimilar nodes) apart in the representation space. Despite the success of the existing GCL methods, they primarily sample node pairs based on the node-level proximity yet the community structures have rarely been taken into consideration. As a result, two nodes from the same community might be sampled as a negative pair. We argue that the community information should be considered to identify node pairs in the same communities, where the nodes insides are semantically similar. To address this issue, we propose a novel Graph Communal Contrastive Learning ($gCooL$) framework to jointly learn the community partition and learn node representations in an end-to-end fashion. Specifically, the proposed $gCooL$ consists of two components: a Dense Community Aggregation ($DeCA$) algorithm for community detection and a Reweighted Self-supervised Cross-contrastive ($ReSC$) training scheme to utilize the community information. Additionally, the real-world graphs are complex and often consist of multiple views. In this paper, we demonstrate that the proposed $gCooL$ can also be naturally adapted to multiplex graphs. Finally, we comprehensively evaluate the proposed $gCooL$ on a variety of real-world graphs. The experimental results show that the $gCooL$ outperforms the state-of-the-art methods.