Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets

TL;DR

This paper introduces a comprehensive benchmark and datasets for Temporal Graph Clustering (TGC), addressing key challenges and providing tools to advance research in this emerging area.

Contribution

It proposes the BenchTGC framework and datasets, enhancing existing clustering techniques for temporal graphs and facilitating standardized evaluation.

Findings

BenchTGC verifies the effectiveness of the benchmark.

Datasets are suitable for TGC tasks.

Highlights the importance of TGC in real-world scenarios.

Abstract

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.