Community extraction for social networks

TL;DR

This paper introduces a new community extraction method for social networks that isolates individual communities without forcing all nodes into partitions, improving accuracy especially for nodes outside clear communities.

Contribution

The paper proposes a novel community extraction framework that focuses on extracting one community at a time, addressing limitations of traditional partitioning methods.

Findings

Method performs well on simulated networks

Method performs well on real-world networks

Asymptotic consistency established under block model

Abstract

Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields, with applications ranging from citation and friendship networks to food webs and gene regulatory networks. Most of the existing community detection methods focus on partitioning the entire network into communities, with the expectation of many ties within communities and few ties between. However, many networks contain nodes that do not fit in with any of the communities, and forcing every node into a community can distort results. Here we propose a new framework that focuses on community extraction instead of partition, extracting one community at a time. The main idea behind extraction is that the strength of a community should not depend on ties between members of other communities, but only on ties within that community and its ties to the outside world. We show that the new extraction criterion performs well on simulated and real networks, and establish asymptotic consistency of our method under the block model assumption.