In this paper, we propose a multi-layer ant-based algorithm MABA, whichdetects communities from networks by means of locally optimizing modularityusing individual ants. The basic version of MABA, namely SABA, combines aself-avoiding label propagation technique with a simulated annealing strategyfor ant diffusion in networks. Once the communities are found by SABA, thismethod can be reapplied to a higher level network where each obtained communityis regarded as a new vertex. The aforementioned process is repeatediteratively, and this corresponds to MABA. Thanks to the intrinsic multi-levelnature of our algorithm, it possesses the potential ability to unfoldmulti-scale hierarchical structures. Furthermore, MABA has the ability thatmitigates the resolution limit of modularity. The proposed MABA has beenevaluated on both computer-generated benchmarks and widely used real-worldnetworks, and has been compared with a set of competitive algorithms.Experimental results demonstrate that MABA is both effective and efficient (innear linear time with respect to the size of network) for discoveringcommunities.
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