Community-aware graph debiased contrastive representation learning

Unsupervised attribute graph representation learning allows for embedding node information into compact vectors without relying on any labels, which greatly facilitates downstream tasks. Graph contrastive learning, founded on the principle of maximizing mutual information, has emerged as a pivotal technique in unsupervised graph representation learning. It achieves node discriminative representations by bringing positive samples closer together and pushing negative samples further apart. However, most existing graph contrastive learning methods primarily concentrate on node-level comparisons, capturing highly abstract node differences to discriminate them, while overlooking the wealth of information present in community substructures within a graph. Additionally, nodes within the same community in a graph often exhibit similar semantics, and considering all other nodes as negative samples unavoidably leads to sampling bias issues. In this work, we propose Community-aware unsupervised graph debiased contrastive representation learning (CAGDCL). Specifically, CAGDCL employs a novel edge-density driven contrastive objective on the augmented graph for community detection to generate robust community prototypes. We introduce the node-prototype contrastive objective based on the node-level contrastive objective. The primary purpose is to encourage the encoder to capture more community-related semantics, enabling the obtained embeddings to maintain intra-community alignment and inter-community uniformity in the embedding space. To mitigate the issue of sampling bias, we propose a weighting scheme of negative samples based on community assignment result and community prototype similarity. Through extensive experiments on several real-world datasets, we demonstrate the effectiveness of CAGDCL.