Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs

Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs introduces Cluster‑TNN, a framework that lifts graph data locally instead of globally, enabling efficient mini‑batch construction for higher‑order domains such as hypergraphs and simplicial complexes. By partitioning the input graph and dynamically sampling node clusters at runtime, Cluster‑TNN reduces peak GPU memory usage by an average of 83.2% across 21 comparisons while preserving connectivity needed for topological structures. This approach allows the first training of multiple higher‑order Topological Neural Networks on large datasets like Reddit and OGBN Products, demonstrating a general strategy for scaling Topological Deep Learning beyond global domain construction.

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