Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling

2026-08-04

AI Summary

This work addresses the challenge of efficiently and accurately clustering temporal graphs to obtain coarse-grained representations when node attributes are missing or weak. The authors propose a theory-driven temporal graph pooling method that formulates community detection as a pooling operator grounded in spectral graph theory. By integrating graph neural networks with multi-slice modularity optimization and incorporating GPU-accelerated spectral clustering and stochastic block models, the approach yields a scalable clustering primitive that remains effective even in attribute-scarce settings. Empirical results demonstrate that while the method excels in scenarios lacking strong attribute signals, neural models achieve superior performance when structural, temporal, and attribute information align coherently. This study thus establishes a new pathway for temporal graph coarsening that balances theoretical rigor with computational efficiency.

Abstract

This work focuses on the problem of learning on temporal graphs, with particular emphasis on the task of clustering: obtaining coarse-grained representations by aggregating information from nodes, edges, and temporal dynamics - a task related to pooling in machine learning on graphs, or community detection in network science. Although graph neural networks reach state-of-the-art performance across many downstream graph tasks, their advantage over established descriptive and inferential clustering algorithms is far less settled, especially under demands of efficiency and recovery accuracy. We frame this tension through three linked perspectives: principles, connecting graph learning and community detection through shared spectral foundations and detectability thresholds in stochastic block model regimes; primitives, making spectral clustering and multislice modularity optimization tractable through GPU-accelerated temporal backends; and pooling, viewing principled community detection as a theory-grounded coarse-graining operator for temporal graphs. Our results indicate that algorithmic methods remain the appropriate tool where attributes are absent or weak - scalability rather than accuracy being the binding obstacle - while neural models are most compelling when structural, temporal, and attribute signals align. By making temporal clustering scalable, GPU-accelerated primitives suggest a route toward theory-grounded pooling, while raising a central question: when does community-based coarse-graining preserve the dynamics needed for downstream learning tasks?

Problem

• temporal graphs
• clustering
• pooling
• community detection
• graph learning

Innovation

• temporal graphs
• graph clustering
• spectral methods
• GPU-accelerated primitives
• theory-grounded pooling