EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks

Sai Karthik Navuluru; Siddhartha Shankar Das; Franck Dernoncourt; S M Ferdous; Ryan A. Rossi; Nesreen K. Ahmed; Baris Coskunuzer; Alex Pothen; Lakshman Tamil; Mahantesh M Halappanavar

arXiv · 2610.09059

EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks

Sai Karthik Navuluru·Siddhartha Shankar Das·Franck Dernoncourt·S M Ferdous·Ryan A. Rossi·Nesreen K. Ahmed·Baris Coskunuzer·Alex Pothen·Lakshman Tamil·Mahantesh M Halappanavar

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Abstract

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 across epochs can require repeated sampling or recomputation. We introduce EDiS (Edge-Disjoint Subgraph sparsification framework), which separates one-time structural extraction from per-epoch graph composition. EDiS decomposes the graph once into cacheable edge-disjoint subgraphs, then recombines them into graphs with edge-budget constraints across epochs and retention ratios without re-extracting structure. Our default construction uses feature-based scores and successive maximum score covering forests, while the same composition mechanism also supports alternative edge selection rules. We provide a combinatorial analysis of the per-epoch sampler, the composition step that draws a training graph from the cached decomposition. We show that, under the default covering-forest selector, the stored decomposition deterministically preserves high-score cut edges, and we derive a selector-agnostic conditional bound on high-score cut survival in composed training graphs. Across 19 homophilic, heterophilic, and large-scale node classification benchmarks against 17 baselines under the same edge budget, EDiS achieves the highest mean benchmark score (accuracy/ROC-AUC) and the lowest average rank and gap-to-best among ranked methods. Ablations show the clearest benefits of structural decomposition and epoch variation at tight edge budgets.

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Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt, S M Ferdous, Ryan A. Rossi, Nesreen K. Ahmed, Baris Coskunuzer, Alex Pothen, Lakshman Tamil, Mahantesh M Halappanavar. 2026-10-06. EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks. https://arxiv.org/abs/2610.09059

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