EDiS decomposes a graph once into cacheable edge-disjoint subgraphs and tops 19 node-classification benchmarks at matched edge budgets
EDiS separates one-time structural extraction from per-epoch graph composition: it decomposes the graph once into cacheable edge-disjoint subgraphs using feature-based scores and successive maximum-score covering forests, then recombines them into training graphs under edge-budget constraints without re-extracting structure; across 19 homophilic, heterophilic, and large-scale node-classification benchmarks at the same edge budget against 17 baselines, EDiS-Lite achieves the highest mean benchmark score and the lowest average rank and gap-to-best.