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.

社区结构在现实世界的网络中无处不在，描绘了节点的强大本地集群。揭露网络的社区结构被认为是了解其动态的关键步骤。实际上，大多数现实世界的网络都是动态的，其社区结构也随着时间而发展。如何揭示这些动态社区最近已成为一个紧迫的问题。本文提出了一种称为MSGA的进化方法，用于准确识别网络中的动态社区。首先，我们提出时间渐近性突跳（TAS），这是一种评估动态网络快照上分区质量的有效措施。然后，我们开发临时合并和拆分运算符，以低成本执行信息导向的大规模搜索。最后，大规模搜索 结合经典的遗传算子，可以为网络的每个快照提供更好的解决方案。MSGA不需要指定建议的社区数。它可以打破分辨率限制并满足时间平滑性约束。实验结果表明，MSGA在综合网络和实际网络上均优于其他最新方法。