  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.
