A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks

Abstract

In real-world social networks, there is an increasing interest in tracking the evolution of groups of users and detecting the various changes they are liable to undergo. Several approaches have been proposed for this. In studying these approaches, we observed that most of them use a two-stage process. In the first stage, they run an algorithm to identify groups of users at each timestamp. In the second stage, a pairwise comparison based on a similarity measure is employed to track groups of users and detect changes they may undergo. While the majority of existing approaches use a two-stage process, they all run different algorithms to identify communities and rely on different similarity measures to track groups of users over time. Noting that the different approaches may perform differently depending on the dynamic social network under investigation, we decided to make a high level survey of some existing tracking approaches and then do a comparative analysis of some of them. In our analysis, we compared the algorithms in two main situations: (1) when groups of users do not overlap and (2) when the groups are overlapping. The study was done on three different testbeds extracted from the DBLP, Autonomous System (AS) and Yelp datasets.

Community Intervention System: COVID-19 Control in Inner Mongolia Autonomous Region, China

“…Scholars emphasize the importance of community culture, which units the residents [ 14 ]. The community’s inner tendencies toward the self-stratification help to optimize its own working pattern [ 15 ]. Research on the externalities of the community focuses on discussing the interaction between the community and other organizations [ 16 , 17 ].…”