  Analyzing the groups in the network based on same attributes, functions or
connections between nodes is a way to understand network information. The task
of discovering a series of node groups is called 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 3 types of datasets show that MAGIC achieves large improvements
over 4 state-of-the-art methods in terms of 4 widely-used metrics.

Social and Information Networks;Machine Learning