发表机构
EPFL; Ruđer Bošković Institute; University of Zagreb; UC Santa Barbara; Capital One（洛桑联邦理工学院; 鲁杰尔·博什科维奇研究所; 萨格勒布大学; 加利福尼亚大学圣巴巴拉分校; 第一资本金融公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大图拓扑深度学习的全局物化瓶颈，提出Cluster-TNN框架，通过局部提升生成拓扑小批量，降低峰值GPU内存83.2%，首次实现多种高阶拓扑神经网络在Reddit等大型数据集上的训练。
AI 中文摘要
拓扑深度学习将基于图的学习扩展到超图、胞腔和单纯复形等高阶领域，这些领域通常通过图提升过程从输入图的模式构建而来。全域训练在模型执行前构建并存储完整的提升表示，在Reddit（23.3万个节点、5730万条边）这类大型密集数据集上，这种全局物化会成为严重的计算瓶颈，常导致训练无法进行。为解决该局限，我们提出Cluster-TNN，一种与领域无关的框架，通过局部提升避免此瓶颈：预处理阶段对输入图分区后，运行时Cluster-TNN动态采样节点簇组，重构其诱导子图以形成小批量，并在每个小批量内应用选定的提升操作。保留采样节点间的所有边，可维持跨簇构建高阶结构所需的连通性，生成现有拓扑神经网络可直接处理的拓扑小批量。在与全图执行的21组匹配对比中，Cluster-TNN在所有配置下均降低了峰值GPU内存，平均降低83.2%，同时保持了有竞争力的预测性能。值得注意的是，这种内存减少使我们首次在Reddit和OGBN Products等大型数据集上训练多种不同的高阶拓扑神经网络，这些结果确立了Cluster-TNN是将拓扑深度学习扩展至全局域构建局限之外的通用策略。
英文摘要
Topological Deep Learning extends graph-based learning to higher-order domains, such as hypergraphs, cellular, and simplicial complexes. These domains are typically constructed from patterns in an input graph through a process of graph lifting. Full-domain training constructs and stores the complete lifted representation before model execution. On large and dense datasets like Reddit (233k nodes and 57.3M edges), this global materialization becomes a severe computational bottleneck, often rendering training infeasible. To address this limitation, we introduce Cluster-TNN, a domain-agnostic framework that avoids this bottleneck by lifting locally instead. After partitioning the input graph during preprocessing, at runtime Cluster-TNN dynamically samples groups of node clusters, reconstructs their induced subgraphs to form mini-batches, and applies the chosen lifting within each mini-batch. Retaining all edges among sampled nodes preserves the connectivity needed to construct higher-order structures across clusters, producing topological mini-batches that existing Topological Neural Networks can process directly. Across 21 matched comparisons with full-graph execution, Cluster-TNN reduces peak GPU memory in every configuration, by 83.2% on average while maintaining competitive predictive performance. Notably, such a reduction enables, to our knowledge, the first training of multiple different higher-order Topological Neural Networks on large datasets such as Reddit and OGBN Products. These results establish Cluster-TNN as a general strategy for scaling Topological Deep Learning beyond the limitations of global domain construction.
