AI 中文总结
该研究提出扩散引导的对比学习框架，结合图神经网络实现时序图的可扩展动态社区检测，在合成网络和OpenAlex合作网络上验证了方法的有效性与可扩展性。
AI 中文摘要
时序图上的动态社区检测旨在识别随时间演变的社区结构，允许节点成员身份随时间变化。本研究将动态社区检测建模为针对观测到的节点-时间实例的任务，为时序交互流中的每个节点-时间实例分配一个簇标签。我们提出一种扩散引导的对比学习框架，该框架利用局部时序扩散亲和矩阵构建正、负节点-时间对，并根据时序结构关系组织学习到的表示。随后，我们对得到的嵌入空间应用聚类算法以检测动态社区。在合成时序网络上的实验表明，所提方法在AMI和ARI指标上优于静态社区检测基线，且与现有动态社区检测方法相比具有竞争力或更优性能，同时保持良好的可扩展性。我们还将该方法应用于2016至2025年的大规模OpenAlex计算机科学合作网络，揭示了真实科学数据中持续存在且不断演变的合作社区。这些结果表明，节点-时间级表示学习为时序图上的可扩展动态社区检测提供了有效框架。
英文摘要
Dynamic community detection on temporal graphs seeks to identify evolving community structures while allowing node memberships to change over time. In this work, we formulate dynamic community detection over observed node-time instances, where each node-time instance in the temporal interaction stream is assigned a cluster label. We propose a diffusion-guided contrastive learning framework that uses a local temporal diffusion affinity matrix to construct positive and negative node-time pairs and organise the learned representations according to their temporal structural relationships. We then apply a clustering algorithm to the resulting embedding space to detect dynamic communities. Experiments on synthetic temporal networks show that the proposed method outperforms static community detection baselines and achieves competitive or better performance than existing dynamic community detection methods in terms of AMI and ARI, while maintaining good scalability. We further apply the method to a large-scale OpenAlex computer science collaboration network from 2016 to 2025, revealing persistent and evolving collaboration communities in real scientific data. These results suggest that time-node-level representation learning provides an effective framework for scalable dynamic community detection on temporal graphs.
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