Community Vitality in Dynamic Temporal Networks

Abstract

Current researches on temporal networks mainly tend to detect community structure. A number of community detection algorithms can obtain community structure on each time slice or each period of time but rarely present the evolution of community structure. Some papers discussed the process of community structure evolution but lacked quantifying the evolution. In this paper, we put forward the concept of Community Vitality (CV), which shows a community's life intensity on a time slice. In the process of computing CV, the “dead communities” can also be distinguished. Moreover, CV cannot only be used to quantify the life intensity of a community but also be used to describe the process of community evolution over time. More specifically, the change of community's structure will be found if CVs for different time slices of a community were compared, while the community with big value of CV can be selected if CVs for different communities were compared. Furthermore, community vitality change rate (CVCR) is proposed for revealing communities' structure change. The results of our experiments show that community vitality is a novel and effective way to understand or model the community evolution.