Terminal-Set-Enhanced Community Detection in Social Networks

TL;DR

This paper introduces a novel community detection approach in social networks using terminal sets, providing approximation algorithms and randomized methods that effectively identify high-quality communities in benchmark networks.

Contribution

It proposes a new terminal set-based framework for community detection, including a polynomial-time 2-approximation algorithm and randomized algorithms for constrained cases.

Findings

Algorithms produce high-quality communities in benchmark tests

Effective for both general and restricted community detection problems

High probability of finding optimal partitions in customized scenarios

Abstract

Community detection aims to reveal the community structure in a social network, which is one of the fundamental problems. In this paper we investigate the community detection problem based on the concept of terminal set. A terminal set is a group of users within which any two users belong to different communities. Although the community detection is hard in general, the terminal set can be very helpful in designing effective community detection algorithms. We first present a 2-approximation algorithm running in polynomial time for the original community detection problem. In the other issue, in order to better support real applications we further consider the case when extra restrictions are imposed on feasible partitions. For such customized community detection problems, we provide two randomized algorithms which are able to find the optimal partition with a high probability. Demonstrated by the experiments performed on benchmark networks the proposed algorithms are able to produce high-quality communities.