CommunityGAN: Community Detection with Generative Adversarial Nets

TL;DR

CommunityGAN introduces a novel framework combining generative adversarial networks with community detection, enabling the identification of overlapping communities and improving upon existing methods in real-world and synthetic networks.

Contribution

The paper presents a new community detection approach that jointly learns graph embeddings indicating community memberships using a GAN-based model, addressing overlap issues.

Findings

Outperforms state-of-the-art community detection methods

Effectively detects overlapping communities in real-world networks

Achieves significant improvements on synthetic data

Abstract

Community detection refers to the task of discovering groups of vertices sharing similar properties or functions so as to understand the network data. With the recent development of deep learning, graph representation learning techniques are also utilized for community detection. However, the communities can only be inferred by applying clustering algorithms based on learned vertex embeddings. These general cluster algorithms like K-means and Gaussian Mixture Model cannot output much overlapped communities, which have been proved to be very common in many real-world networks. In this paper, we propose CommunityGAN, a novel community detection framework that jointly solves overlapping community detection and graph representation learning. First, unlike the embedding of conventional graph representation learning algorithms where the vector entry values have no specific meanings, the embedding of CommunityGAN indicates the membership strength of vertices to communities. Second, a specifically designed Generative Adversarial Net (GAN) is adopted to optimize such embedding. Through the minimax competition between the motif-level generator and discriminator, both of them can alternatively and iteratively boost their performance and finally output a better community structure. Extensive experiments on synthetic data and real-world tasks demonstrate that CommunityGAN achieves substantial community detection performance gains over the state-of-the-art methods.