A Novel Community Detection Method of Social Networks for the Well-Being of Urban Public Spaces

Abstract

A third place (public social space) has been proven to be a gathering place for communities of friends on social networks (social media). The regulars at places of worship, cafes, parks, and entertainment can also possibly be friends with those who follow each other on social media, with other non-regulars being social network friends of one of the regulars. Therefore, detecting and analyzing user-friendly communities on social networks can provide references for the layout and construction of urban public spaces. In this article, we focus on proposing a method for detecting communities of signed social networks and mining gamma-Quasi-Cliques for closely related users within them. We fully consider the relationship between friends and enemies of objects in signed networks, consider the mutual influence between friends or enemies, and propose a novel method to recompute the weighted edges between nodes and mining gamma-Quasi-Cliques. In our experiment, with a variety of thresholds given, we conducted multiple sets of tests via real-life social network datasets, compared various reweighted datasets, and detected maximal balanced gamma-Quasi-Cliques to determine the optimal parameters of our method.