An Ant-Based Algorithm with Local Optimization for Community Detection in Large-Scale Networks
Dongxiao He, Jie Liu, Bo Yang, Yuxiao Huang, Dayou Liu, Di Jin

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…
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