  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.
