3 papers
cs.LG2026
EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks
Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt +7
Sparse GNN training reduces computation, but deciding which edges to keep can be costly. Reusing one sparse graph is cheap, but locks training to a fixed topology, while varying it…
cs.LG2026
Scaffold: Support Graph Theory Based Sparsification for Graph Neural Networks
Siddhartha Shankar Das, Sai Karthik Navuluru, S M Ferdous +7
Graph neural networks (GNNs) rely on message passing over graph edges, making their computational and memory costs strongly dependent on graph density. Graph sparsification offers…
cs.DS2026
Succinct Graph Representations and Algorithmic Applications
Ahammed Ullah, Alex Pothen
We propose new graph representations that exploit dense local structure to improve time and space simultaneously. Given an undirected graph , we define a dual clique cover (DCC)…