GCLS$^2$: Towards Efficient Community Detection Using Graph Contrastive Learning with Structure Semantics

TL;DR

GCLS$^2$ introduces a graph contrastive learning framework that effectively captures community structure semantics, improving community detection accuracy and efficiency on large-scale networks.

Contribution

The paper proposes a novel GCL framework incorporating structural semantics and a high-level graph partitioning algorithm for scalable community detection.

Findings

Outperforms nine state-of-the-art methods in accuracy and modularity.

Demonstrates efficiency in large-scale network community detection.

Provides theoretical proof of improved structural representation learning.

Abstract

Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structural semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we will consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS$^2$) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS$^2$ online training. It is worth noting that we prove a lower bound on the training of GCLS$^2$ from the perspective of the information theory, explaining why GCLS$^2$ can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS$^2$ outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities.