  Community structure is pervasive in various real-world networks, portraying
the strong local clustering of nodes. Unveiling the community structure of a
network is deemed to a crucial step towards understanding the dynamics on the
network. Actually, most of the real-world networks are dynamic and their
community structures are evolutionary over time accordingly. How to revealing
the dynamical communities has recently become a pressing issue. Here, we
present an evolutionary method for accurately identifying dynamical communities
in the networks. In this method, we first introduced a fitness function that is
a compound of asymptotic surprise values on the current and previous snapshots
of the network. Second, we developed ad hoc merging and splitting operators,
which allows for large-scale searching while preserving low cost. Third, this
large-scale searching coupled with local mutation and crossover enhanced
revealing a better solution to each snapshot of the network. This method does
not require specifying the number of communities advanced, and free from
resolution limit while satisfying temporal smooth constraint. Experimental
results on both model and real dynamic networks show that the method can find a
better solution compared with state-of-art approaches.
