Community detection is a fundamental task in social network analysis. Online social networks have dramatically increased the volume and speed of interactions among users, enabling advanced analysis of these dynamics. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, most community detection efforts focus on communities within static networks. Here, we describe a framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. To this end, a modularity-based strategy is proposed to effectively detect and track dynamic communities. The potential of our framework is shown by conducting extensive experiments on synthetic networks containing embedded events. Results indicate that our framework outperforms other state-of-the-art methods. In addition, we briefly explore how the proposed approach can identify dynamic communities in a Twitter network composed of more than 60,000 users, which posted over 5 million tweets throughout 2020. The proposed framework can be applied to different social network and provides a valuable tool to understand the evolution of communities in dynamic social networks.
翻译:社区探测是社会网络分析的一项基本任务。在线社会网络大大增加了用户之间互动的数量和速度,从而能够对这些动态进行高级分析。尽管人们对跟踪现实世界社会网络用户群体演变的兴趣日益浓厚,但大多数社区探测工作的重点是静态网络中的社区。在这里,我们描述一个在动态网络中长期跟踪社区的框架,为每个社区确定了一系列重大事件。为此,提议了一个基于模块的战略,以有效检测和跟踪动态社区。通过对包含嵌入事件的合成网络进行广泛的实验,显示了我们框架的潜力。结果显示,我们的框架优于其他最先进的方法。此外,我们简要探讨拟议方法如何在由60 000多个用户组成的推特网络中识别动态社区,该网络在整个2020年张贴了500多万条推特。拟议框架可以适用于不同的社会网络,并为了解动态社会网络中社区的演变提供宝贵的工具。</s>