Many real world systems ranging from biological to social systems can be modeled as networks. As network data becomes more ubiquitous, it is important to reduce the large-scale networks into smaller modules or communities. Most of the original research in community detection has focused on static networks while many real world networks such as social and biological networks exhibit varying topology over time. For this reason, over the past twenty years various community detection methods for dynamic networks have been developed in physics, statistics, computer science and signal processing. In this paper, we present an overview of the current state-of-the-art in community detection for dynamic networks and illustrate some of the shortcomings of existing methods. We also outline future challenges and opportunities in this research area.

从生物系统到社会系统的许多现实世界系统都可以建模为网络。随着网络数据变得越来越普遍，重要的是将大型网络减少为更小的模块或社区。大多数社区检测的原始研究都集中在静态网络上，而许多现实世界的网络，如社会和生物网络，随着时间的推移表现出不同的拓扑结构。出于这个原因，在过去的 20 年里，物理学、统计学、计算机科学和信号处理领域开发了各种动态网络社区检测方法。在本文中，我们概述了动态网络社区检测的当前最新技术，并说明了现有方法的一些缺点。我们还概述了该研究领域的未来挑战和机遇。