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arXiv · 1303.4711

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
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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.

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Dongxiao He, Jie Liu, Bo Yang, Yuxiao Huang, Dayou Liu, Di Jin. 2013-03-19. An Ant-Based Algorithm with Local Optimization for Community Detection in Large-Scale Networks. https://doi.org/10.1142/s0219525912500361

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