A Distributed Hybrid Community Detection Methodology for Social Networks

Abstract

Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the millions or even billions of vertices. Hence, community detection, where groups of vertices are formed according to a well-defined similarity measure, has never been more essential affecting a vast range of scientific fields such as bio-informatics, sociology, discrete mathematics, nonlinear dynamics, digital marketing, and computer science. Even if an impressive amount of research has yet been published to tackle this NP-hard class problem, the existing methods and algorithms have virtually been proven inefficient and severely unscalable. In this regard, the purpose of this manuscript is to combine the network topology properties expressed by the loose similarity and the local edge betweenness, which is a currently proposed Girvan–Newman’s edge betweenness measure alternative, along with the intrinsic user content information, in order to introduce a novel and highly distributed hybrid community detection methodology. The proposed approach has been thoroughly tested on various real social graphs, roundly compared to other classic divisive community detection algorithms that serve as baselines and practically proven exceptionally scalable, highly efficient, and adequately accurate in terms of revealing the subjacent network hierarchy.

A Comparative Analysis of Community Detection Agglomerative Technique Algorithms and Metrics on Citation Network

“…However, unlike divisive hierarchical clustering, in which edges between pairs of vertices with low similarity are removed, in this case inter-cluster edges are removed, and there is no prior guarantee that inter-cluster edges connect vertices with minimal similarity. Instead of removing a single edge, it may be necessary to remove an entire vertex or sub graph [9]. The time complexity of Newmann Girvan Method is O (m 2 n) where m is the number of edges and n is the total number of nodes.…”