Overlapping Community Detection in Temporal Text Networks

Network is a powerful language to represent relational data. One way to understand network is to analyze groups of nodes which share same properties or functions. The task of discovering such groups is known as community detection. Generally, two types of information can be utilized to fulfill this task, i.e., the link structures and the node attributes. The temporal text network is a special kind of network that contains both sources of information. Typical representatives include online blog networks, the World Wide Web (WWW) and academic citation networks.In this paper, we study the problem of overlapping community detection in temporal text network. By examining 32 large temporal text networks, we find a lot of edges connecting two nodes with no common community and discover that nodes in the same community share similar textual contents. This scenario cannot be quantitatively modeled by practically all existing community detection methods. Motivated by these empirical observations, we propose MAGIC (Model Affiliation Graph with Interacting Communities), a generative model which captures community interactions and considers the information from both link structures and node attributes. Our experiments on three categories of datasets show that MAGIC achieves obvious improvements over 4 state-of-theart methods in terms of 4 widely-used metrics. CCS Concepts •Data Mining → Graph Mining; •Networks → Network Clustering;