Scalable Subgraph Sampling via Resistance Curvature

Abstract:Subgraph sampling reduces the training cost of large-scale graph neural networks, but sampling criteria may overlook the geometric roles of edges. We propose a resistance-curvature-guided sampling framework built on ERC-LG, a curvature approximation method for large-scale graphs. ERC-LG combines Johnson-Lindenstrauss projections with regularized multi-GPU batched conjugate gradient solvers, avoiding explicit Laplacian pseudoinverse computation and full embedding storage. The resulting curvature informs node- and edge-sampling probabilities for constructing GNN training subgraphs. Experiments show numerical agreement with pseudoinverse-based curvature and reduced runtime compared with CG-only computation. ERC-LG-based sampling variants achieve the highest mean accuracy on six of seven real-world datasets in downstream node classification.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.27209 [cs.LG]

(or arXiv:2609.27209v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.27209 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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