构建首个完整时序图聚类基准，解决方法与数据缺失问题。
Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets
- 设计交互序列批处理框架，适配时序图聚类需求
- 提出BenchTGC数据集，覆盖真实动态场景
- 为时序图聚类提供可复现的评测标准
时序图聚类（TGC）是一项新兴任务，关注时序图中节点的聚类。相比传统静态图聚类，它通过基于交互序列的批处理模式，在时间与空间需求间实现平衡。然而，两大挑战阻碍其发展：现有聚类技术不适用，公开数据集不匹配。为此，本文提出综合性基准BenchTGC，设计框架阐明时序图聚类范式，并改进现有方法以适配时序图特性。同时，分析现有公开数据集问题，构建多个适用于TGC任务的BenchTGC数据集。大量实验验证了BenchTGC的有效性，凸显了该任务的必要性与重要性。真实世界中动态复杂的场景是时序图聚类的基础。代码与数据已开源：https://github.com/MGitHubL/BenchTGC。
原文摘要 · Abstract (English)
Temporal Graph Clustering (TGC) is a new task with little attention, focusing on node clustering in temporal graphs. Compared with existing static graph clustering, it can find the balance between time requirement and space requirement (Time-Space Balance) through the interaction sequence-based batch-processing pattern. However, there are two major challenges that hinder the development of TGC, i.e., inapplicable clustering techniques and inapplicable datasets. To address these challenges, we propose a comprehensive benchmark, called BenchTGC. Specially, we design a BenchTGC Framework to illustrate the paradigm of temporal graph clustering and improve existing clustering techniques to fit temporal graphs. In addition, we also discuss problems with public temporal graph datasets and develop multiple datasets suitable for TGC task, called BenchTGC Datasets. According to extensive experiments, we not only verify the advantages of BenchTGC, but also demonstrate the necessity and importance of TGC task. We wish to point out that the dynamically changing and complex scenarios in real world are the foundation of temporal graph clustering. The code and data is available at: https://github.com/MGitHubL/BenchTGC.
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