Urban Regional Social Community Detection Using Location Based Social Network Big Data

Abstract

In this paper, we propose a methodology of applying location based social network (LBSN) Big Data to detect urban regional social communities (URSCs) and analyze their activation levels. For this, we first construct a social spatial network (SSN) based on the LBSN Big Data of a city. Then, by applying a modularity optimization algorithm to the SSN constructed, where modularity is a measure to check the strength of clustered networks, we detect the boundaries of the URSCs. The activation level of each detected URSC is further analyzed based on a diversity index, i.e., Shannon entropy. For experiments, we apply the proposed methodology to the city of Seoul where the LBSN Big Data is collected from Foursquare social networks. Through the experimental results, we observe that the detected URSCs match well with the URSCs known by the Seoul citizen from which we can confirm the effectiveness of our proposed methodology in detecting USRCs and analyzing their activation levels