Integrating Group Homophily and Individual Personality of Topics Can Better Model Network Communities

Community detection is an important research field in the understanding of networks. The definition of network communities focuses on denser intracommunity links and sparpser intercommunity links. It cannot explain the fundamental generation mechanisms of the two types of links, which is challenging to reveal. Unfortunately, none of existing works can solve this challenge which is important for accurately modeling community structures. This paper investigates a typical category of networks which possess contents on links. Based on analyses of real networks, we get an observation that nodes with distinctive personality regarding content topics are more active across communities, while nodes without it are more active inside a community, behaving in a similar way known as homophily. This observation provides clues to the generation of intracommunity and intercommunity links. Based on above observation, this paper proposes a novel generative community detection model called GHIPT (Group Homophily and Individual Personality of Topics) by integrating group homophily and individual personality of topics. Besides deriving more precise community results by accurately modeling intracommunity and intercommunity links, GHIPT is able to identify those nodes with distinctive personality who are more willing to interact with others from different communities. It further validates that they change their community memberships more frequently. GHIPT is evaluated on two real networks, i.e., Reddit and DBLP. Experimental results show that it outperforms all the state-of-the-art baselines. In addition to case studies on above two datasets, a case study on COVID-19 dataset provides new insights to support the ongoing fight against COVID-19 pandemic.