CommunityGAN: Community Detection with Generative Adversarial Nets

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.

11 pages, 9 figures, 7 tables

References in corpus (8)

• Modularity and community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Detecting the overlapping and hierarchical community structure of complex networks
• Gradient Estimation Using Stochastic Computation Graphs
• Adversarial Network Embedding
• Using Triangles to Improve Community Detection in Directed Networks
• Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
• Neighborhoods are good communities

Cited by in corpus (2)

• vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
• A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks