Ant Colony Optimization with a New Random Walk Model for Community Detection in Complex Networks

TL;DR

This paper introduces MACO, a novel ant colony optimization algorithm based on Markov random walks, designed to effectively detect community structures in complex networks through iterative pheromone updates and clustering ensemble methods.

Contribution

The paper proposes a new MACO algorithm that integrates Markov random walks into ant colony optimization for community detection, enhancing convergence and accuracy.

Findings

MACO outperforms existing algorithms on synthetic benchmarks.

MACO effectively detects communities in real-world networks.

The approach converges reliably to clear community structures.

Abstract

Detecting communities from complex networks has recently triggered great interest. Aiming at this problem, a new ant colony optimization strategy building on the Markov random walks theory, which is named as MACO, is proposed in this paper. The framework of ant colony optimization is taken as the basic framework in this algorithm. In each iteration, a Markov random walk model is employed as heuristic rule; all of the ants local solutions are aggregated to a global one through an idea of clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progressive strengthening of within-community links and the weakening of between-community links. Gradually this converges to a solution where the underlying community structure of the complex network will become clearly visible. The proposed MACO has been evaluated both on synthetic benchmarks and on some real-world networks, and compared with some present competing algorithms. Experimental result has shown that MACO is highly effective for discovering communities.