Community detection is a crucial task to unravel the intricate dynamics of online social networks. The emergence of these networks has dramatically increased the volume and speed of interactions among users, presenting researchers with unprecedented opportunities to explore and analyze the underlying structure of social communities. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, the predominant focus of community detection efforts has been on communities within static networks. In this paper, we introduce a novel framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. Our framework adopts a modularity-based strategy and does not require a predefined threshold, leading to a more accurate and robust tracking of dynamic communities. We validated the efficacy of our framework through extensive experiments on synthetic networks featuring embedded events. The results indicate that our framework can outperform the state-of-the-art methods. Furthermore, we utilized the proposed approach on a Twitter network comprising over 60,000 users and 5 million tweets throughout 2020, showcasing its potential in identifying dynamic communities in real-world scenarios. The proposed framework can be applied to different social networks and provides a valuable tool to gain deeper insights into the evolution of communities in dynamic social networks.

翻译：社区检测是揭示在线社交网络复杂动态的关键任务。这些网络的出现极大增加了用户间交互的规模与速度，为研究者探索和分析社交社区的底层结构提供了前所未有的机遇。尽管追踪真实社交网络中用户群组演化的研究日益增多，但社区检测工作仍主要聚焦于静态网络中的社区。本文提出了一种新颖的动态网络社区时序追踪框架，能够为每个社区识别一系列关键事件。该框架采用基于模块度的策略，无需预设阈值，从而实现了更准确、更鲁棒的动态社区追踪。通过在嵌入事件的合成网络上进行广泛实验，我们验证了框架的有效性。结果表明，所提框架能够超越当前最先进方法。此外，我们将该方法应用于包含2020年间超6万名用户及500万条推文的Twitter网络，展示了其在真实场景中识别动态社区的潜力。该框架可适用于不同社交网络，为深入理解动态社交网络中社区的演化提供了宝贵工具。