Tracking the evolution of community structures in time-evolving social networks

In real-world social networks, there is increasing interest in tracking the evolution of groups of users. Existing approaches track evolving communities, in a time-sequential way, by comparing communities in terms of nodes using a similarity measure such as the Jaccard or a modified Jaccard measure. The measure allows the use of a one-to-one comparison in order to match communities. However, tracking a given community based on this measure alone may, at the end of its lifespan yield a community that does not share any node with the community initially observed. In this paper we present a novel approach for modeling and detecting the evolution of communities. In our model, we first build a matrix that counts the number of nodes shared between two communities. The individual rows of the obtained matrix are then used to represent nodes shared by a community with all other communities over time. This effectively captures the trace of the communities that should be compared over the period of observation. We then propose a new similarity measure, named mutual transition, for tracking the communities and rules for capturing significant transition events a community can undergo. The proposed approach is general in the sense that it can be applied to different social networks. To demonstrate the suitability of the proposed method, we conducted experiments on real data extracted from the DBLP, Autonomous System and YELP.