DCC: A Cascade based Approach to Detect Communities in Social Networks

TL;DR

This paper introduces DCC, a novel cascade-based method for community detection in social networks that leverages tie strength and cascade information to identify densely connected community cores.

Contribution

The paper presents a new local density measure for tie strength and a cascade-guided propagation approach for community detection, improving accuracy over existing methods.

Findings

DCC outperforms baseline algorithms in community detection accuracy.

The tie strength measure effectively identifies community cores.

Cascade-based propagation improves detection in dense networks.

Abstract

Community detection in Social Networks is associated with finding and grouping the most similar nodes inherent in the network. These similar nodes are identified by computing tie strength. Stronger ties indicates higher proximity shared by connected node pairs. This work is motivated by Granovetter's argument that suggests that strong ties lies within densely connected nodes and the theory that community cores in real-world networks are densely connected. In this paper, we have introduced a novel method called \emph{Disjoint Community detection using Cascades (DCC)} which demonstrates the effectiveness of a new local density based tie strength measure on detecting communities. Here, tie strength is utilized to decide the paths followed for propagating information. The idea is to crawl through the tuple information of cascades towards the community core guided by increasing tie strength. Considering the cascade generation step, a novel preferential membership method has been developed to assign community labels to unassigned nodes. The efficacy of $DCC$ has been analyzed based on quality and accuracy on several real-world datasets and baseline community detection algorithms.