Criterions for locally dense subgraphs

TL;DR

This paper introduces a new local community detection method based on separation and internal cohesion, providing a step towards a precise community definition in complex networks.

Contribution

It proposes a novel local optimization approach utilizing separation and internal cohesion, including the concept of internal cohesion, for community detection.

Findings

Effective on benchmark datasets

Outperforms some existing methods

Introduces the concept of internal cohesion

Abstract

Community detection is one of the most investigated problems in the field of complex networks. Although several methods were proposed, there is still no precise definition of communities. As a step towards a definition, I highlight two necessary properties of communities, separation and internal cohesion, the latter being a new concept. I propose a local method of community detection based on two-dimensional local optimization, which I tested on common benchmarks and on the word association database.