  Community detection is one of the most important and interesting issues in
social network analysis. In recent years, simultaneous considering of nodes'
attributes and topological structures of social networks in the process of
community detection has attracted the attentions of many scholars, and this
consideration has been recently used in some community detection methods to
increase their efficiencies and to enhance their performances in finding
meaningful and relevant communities. But the problem is that most of these
methods tend to find non-overlapping communities, while many real-world
networks include communities that often overlap to some extent. In order to
solve this problem, an evolutionary algorithm called MOBBO-OCD, which is based
on multi-objective biogeography-based optimization (BBO), is proposed in this
paper to automatically find overlapping communities in a social network with
node attributes with synchronously considering the density of connections and
the similarity of nodes' attributes in the network. In MOBBO-OCD, an extended
locus-based adjacency representation called OLAR is introduced to encode and
decode overlapping communities. Based on OLAR, a rank-based migration operator
along with a novel two-phase mutation strategy and a new double-point crossover
are used in the evolution process of MOBBO-OCD to effectively lead the
population into the evolution path. In order to assess the performance of
MOBBO-OCD, a new metric called alpha_SAEM is proposed in this paper, which is
able to evaluate the goodness of both overlapping and non-overlapping
partitions with considering the two aspects of node attributes and linkage
structure. Quantitative evaluations reveal that MOBBO-OCD achieves favorable
results which are quite superior to the results of 15 relevant community
detection algorithms in the literature.
