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.