A Local Method for Detecting Communities

TL;DR

This paper introduces a new local community detection method that is computationally efficient, physically meaningful, and applicable to both local and global network analysis, demonstrated on various artificial and real-world networks.

Contribution

It presents a novel local community detection technique that does not require full network knowledge and is computationally inexpensive, with a global application extension.

Findings

Effective on artificial and real-world networks

Successfully applied to Zachary Karate club network

Provides physically meaningful community structures

Abstract

We propose a novel method of community detection that is computationally inexpensive and possesses physical significance to a member of a social network. This method is unlike many divisive and agglomerative techniques and is local in the sense that a community can be detected within a network without requiring knowledge of the entire network. A global application of this method is also introduced. Several artificial and real-world networks, including the famous Zachary Karate club, are analyzed.