Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevertheless, deep clustering for temporal graphs, which could capture crucial dynamic interaction information, has not been fully explored. It means that in many clustering-oriented real-world scenarios, temporal graphs can only be processed as static graphs. This not only causes the loss of dynamic information but also triggers huge computational consumption. To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. In addition, we discuss differences between temporal graph clustering and static graph clustering from several levels. To verify the superiority of the proposed framework TGC, we conduct extensive experiments. The experimental results show that temporal graph clustering enables more flexibility in finding a balance between time and space requirements, and our framework can effectively improve the performance of existing temporal graph learning methods. The code is released: https://github.com/MGitHubL/Deep-Temporal-Graph-Clustering.

翻译：深度图聚类因其在无监督场景下增强模型表示学习能力而近期受到广泛关注。然而，能够捕获关键动态交互信息的时序图深度聚类尚未得到充分探索。这意味着在许多面向聚类的真实场景中，时序图只能被当作静态图处理，这不仅导致动态信息的丢失，还会引发巨大的计算消耗。为解决该问题，我们提出了一种名为TGC的深度时序图聚类通用框架，该框架引入深度聚类技术以适应时序图基于交互序列的批处理模式。此外，我们从多个层面讨论了时序图聚类与静态图聚类之间的差异。为验证所提框架TGC的优越性，我们开展了大量实验。实验结果表明，时序图聚类在平衡时间和空间需求方面具有更高的灵活性，且我们的框架能够有效提升现有时序图学习方法的性能。代码已开源：https://github.com/MGitHubL/Deep-Temporal-Graph-Clustering。