时序图上的学习与聚类：原理、基元与池化
Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling
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中文总结 AI 辅助
该研究针对时序图聚类任务，从原理、基元与池化三方面展开，提出 GPU 加速基元实现高效时序聚类，为理论导向的池化提供路径，明确了算法与神经模型的适用场景。
中文摘要 AI 辅助
本研究聚焦于时序图上的学习问题，尤其侧重聚类任务：通过聚合节点、边及时序动态的信息获取粗粒度表示，该任务与图机器学习中的池化或网络科学中的社区检测相关。尽管图神经网络在众多下游图任务中达到了最优性能，但相较于成熟的描述性与推断式聚类算法，其优势远未明确，尤其在效率与恢复精度的要求下。我们从三个关联视角阐释这一矛盾：原理视角，通过随机块模型 regime 下的共享谱基础与可检测阈值，连接图学习与社区检测；基元视角，通过 GPU 加速的时序后端，使谱聚类与多切片模块度优化变得可行；池化视角，将有原理的社区检测视为时序图的基于理论的粗粒度算子。结果表明，在属性缺失或薄弱时，算法方法仍是合适工具——可扩展性而非精度是关键障碍；而当结构、时序与属性信号一致时，神经模型最具竞争力。通过使时序聚类具备可扩展性，GPU 加速的基元为基于理论的池化提供了路径，同时提出了核心问题：基于社区的粗粒度何时能保留下游学习任务所需的动态？
英文摘要
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?
