Community Detection Across Multiple Social Networks based on Overlapping Users

TL;DR

This paper presents a novel community detection method across multiple social networks that leverages overlapping users and social relevance to identify communities effectively, improving upon existing approaches.

Contribution

It introduces the CMN NMF algorithm for discovering stub communities from overlapping users and extends them across networks based on user similarity.

Findings

Demonstrates improved effectiveness over other methods on real datasets.

Successfully identifies communities across multiple social networks.

Utilizes overlapping users and social relevance for community detection.

Abstract

With the rapid development of Internet technology, online social networks (OSNs) have got fast development and become increasingly popular. Meanwhile, the research works across multiple social networks attract more and more attention from researchers, and community detection is an important one across OSNs for online security problems, such as the user behavior analysis and abnormal community discovery. In this paper, a community detection method is proposed across multiple social networks based on overlapping users. First, the concept of overlapping users is defined, then an algorithm CMN NMF is designed to discover the stub communities from overlapping users based on the social relevance. After that, we extend each stub community in different social networks by adding the users with strong similarity, and in the end different communities are excavated out across networks. Experimental results show the advantage on effectiveness of our method over other methods under real data sets.