Revealing dynamic communities in networks using genetic algorithm with merge and split operators

Community structures are pervasive in real-world networks, portraying the strong local clustering of nodes. Unveiling the community structure of a network is deemed to be a crucial step towards understanding its dynamics. Actually, most real-world networks are dynamic, and their community structures are evolving over time accordingly. How to reveal these dynamic communities has recently become a pressing issue. This paper presents an evolutionary method termed MSGA for accurately identifying dynamic communities in networks. First, we propose temporal asymptotic surprise (TAS), an effective measure to evaluate the quality of a partition on the snapshot of the dynamic network. Then we develop ad-hoc merge and split operators to perform an information-directed large-scale search at a low cost. Finally, large-scale search, coupled with classic genetic operators, are used to reveal a better solution for each snapshot of the network. MSGA does not require specifying the proposed number of communities. It can break the resolution limit and satisfies temporal smoothness constraints. Experimental results show that MSGA outperforms other state-of-the-art approaches on both synthetic networks and real-world networks.