YASCA: A collective intelligence approach for community detection in complex networks

TL;DR

This paper introduces YASCA, a novel ensemble clustering method for community detection in complex networks that leverages ego-centered communities and ensemble ranking to improve accuracy.

Contribution

It proposes a new seed-centric ensemble clustering approach that combines local modularities and ego-centered communities for better community detection.

Findings

Validates approach on real-world networks with known community structures

Shows improved community detection accuracy over existing methods

Demonstrates efficiency of ensemble ranking in community detection

Abstract

In this paper we present an original approach for community detection in complex networks. The approach belongs to the family of seed-centric algorithms. However, instead of expanding communities around selected seeds as most of existing approaches do, we explore here applying an ensemble clustering approach to different network partitions derived from ego-centered communities computed for each selected seed. Ego-centered communities are themselves computed applying a recently proposed ensemble ranking based approach that allow to efficiently combine various local modularities used to guide a greedy optimisation process. Results of first experiments on real world networks for which a ground truth decomposition into communities are known, argue for the validity of our approach.