An Ant-Based Algorithm with Local Optimization for Community Detection in Large-Scale Networks

TL;DR

This paper introduces MABA, a multi-layer ant-based algorithm that effectively detects communities in large-scale networks by optimizing modularity and uncovering hierarchical structures.

Contribution

It presents a novel multi-layer ant-based method combining local optimization and multi-scale detection, addressing modularity resolution limits.

Findings

MABA outperforms existing algorithms in accuracy and efficiency.

It can uncover multi-scale hierarchical community structures.

Demonstrates near-linear runtime on large networks.

Abstract

In this paper, we propose a multi-layer ant-based algorithm MABA, which detects communities from networks by means of locally optimizing modularity using individual ants. The basic version of MABA, namely SABA, combines a self-avoiding label propagation technique with a simulated annealing strategy for ant diffusion in networks. Once the communities are found by SABA, this method can be reapplied to a higher level network where each obtained community is regarded as a new vertex. The aforementioned process is repeated iteratively, and this corresponds to MABA. Thanks to the intrinsic multi-level nature of our algorithm, it possesses the potential ability to unfold multi-scale hierarchical structures. Furthermore, MABA has the ability that mitigates the resolution limit of modularity. The proposed MABA has been evaluated on both computer-generated benchmarks and widely used real-world networks, and has been compared with a set of competitive algorithms. Experimental results demonstrate that MABA is both effective and efficient (in near linear time with respect to the size of network) for discovering communities.