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 adjusts deep clustering techniques (clustering assignment distribution and adjacency matrix reconstruction) to suit the interaction sequence-based batch-processing pattern of temporal graphs. In addition, we discuss differences between temporal graph clustering and existing 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. Our code and supplementary material will be released after publication.

翻译：深度图聚类近年来因其在无监督场景下增强模型表征学习能力而备受关注。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实际场景中，时序图只能作为静态图处理。这不仅导致动态信息丢失，还会引发巨大的计算消耗。为解决这一问题，我们提出了一个通用的深度时序图聚类框架TGC，该框架调整深度聚类技术（聚类分配分布与邻接矩阵重构）以适应基于交互序列的时序图批量处理模式。此外，我们从多个层面讨论了时序图聚类与现有静态图聚类之间的差异。为验证所提框架TGC的优越性，我们进行了大量实验。实验结果表明，时序图聚类在时空需求平衡方面具有更强灵活性，而我们的框架能有效提升现有时序图学习方法的性能。本研究的代码与补充材料将在论文发表后公开。