2012/04/09 by Dongxiao He, Jie Liu, Bo Yang +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Clustering Algorithms Research #Algorithm #Ant colony #Ant colony optimization algorithms #Complex Network Analysis Techniques #Computer science #Data Visualization and Analytics #Mathematical optimization #Mathematics #Modularity (biology) #Simulated annealing #cs.SI #physics.soc-ph
paper · pdf · doi:10.1142/s0219525912500361
published as Advances in Complex Systems, 2012, 15(08): 1250036 · 18 pages,7 figures, 4 tables. arXiv admin note: text overlap with arXiv:0803.0476, arXiv:cond-mat/0309508 by other authors
openalex publication_date 2012/04/09 · arxiv created 2013/03/19 · arxiv updated 2013/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.