Directed Community Detection With Network Embedding
Jingnan Zhang, Xin He, Junhui Wang
Abstract
Community detection in network data aims at grouping similar nodes sharing certain characteristics together. Most existing methods focus on detecting communities in undirected networks, where similarity between nodes is measured by their node features and whether they are connected. In this article, we propose a novel method to conduct network embedding and community detection simultaneously in a directed network. The network embedding model introduces two sets of vectors to represent the out- and in-nodes separately, and thus allows the same nodes belong to different out- and in-communities. The community detection formulation equips the negative log-likelihood with a novel regularization term to encourage community structure among the nodes representations, and thus achieves better performance by jointly estimating the nodes embeddings and their community structures. To tackle the resultant optimization task, an efficient alternative updating scheme is developed. More importantly, the asymptotic properties of the proposed method are established in terms of both network embedding and community detection, which are also supported by numerical experiments on some simulated and real examples.