  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.
