Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets

2025-08-07

IEEE Transactions on Pattern Analysis and Machine Intelligence

AI Summary

Temporal graph clustering (TGC) has been hindered by the absence of dedicated methodologies and high-quality datasets. This work addresses this gap by systematically constructing BenchTGC, the first comprehensive benchmark for TGC, and introducing a unified framework tailored to the dynamic nature of temporal graphs. The framework integrates interaction sequence batching, temporal graph neural networks, and dynamic graph representation learning to adapt and reformulate existing clustering algorithms for temporal settings. Alongside the framework, we release the first dataset suite specifically designed for TGC. Extensive experiments demonstrate that BenchTGC substantially improves clustering performance, underscoring the necessity and effectiveness of specialized TGC approaches in balancing spatiotemporal modeling demands and capturing real-world dynamic graph patterns.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Temporal Graph Clustering
• clustering techniques
• temporal graph datasets
• Time-Space Balance
• node clustering

Innovation

• Temporal Graph Clustering
• BenchTGC
• Time-Space Balance
• Graph Benchmarking
• Dynamic Graph Datasets