这篇 arXiv 论文把图神经网络和传统聚类算法掰开对比，结论很实在：没属性时谱聚类更靠谱，还给出了 GPU 加速的池化思路。
该论文从原理、原语、池化三个视角分析时间图上的聚类问题，连接图学习与社区检测的谱理论基础。研究表明，在节点属性缺失或较弱时，基于谱聚类和多层模块度优化的传统算法仍是更合适的选择，主要瓶颈是可扩展性而非精度。当结构、时间与属性信号对齐时，图神经网络模型优势更明显。作者通过GPU加速的时间后端使谱聚类等原语具备可扩展性，并提出将社区检测作为时间图池化算子的路线。
Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling
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?