[Submitted on 21 Sep 2026]
Title:Dual-GNN Multilevel Coarsening for Maximum Independent Set
View a PDF of the paper titled Dual-GNN Multilevel Coarsening for Maximum Independent Set, by Tianfeng Chen and Xianyue Li
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Abstract:Solving large-scale instances of the Traveling Salesman Problem (TSP) exactly is computationally expensive. Researchers often employ graph sparsification methods to improve computational efficiency. Traditional sparsification methods typically rely on fixed heuristics and fail to fully exploit instance-specific structural information. In this paper, we propose Graph Edge Sparsification (GES), a learning-based sparsification approach for Euclidean TSP. By incorporating geometric structural information and combinatorial optimization technology, our proposed method adaptively generates a sparsification graph for different instances, significantly reducing the graph size and accelerating the solving process. Experimental results demonstrate that our sparsification method can prune up to 95\% of edges on the MATILDA dataset, while keeping the solution gap within 1\% of the optimal value. Moreover, our approach exhibits strong generalization capability on the TSPLIB this http URL some large-scale instances, the pruning rate exceeds 99\%, while the optimality gap remains below 1\%.
Comments: 12 pages, 5 figures, and 6 tables
Subjects:
Machine Learning (cs.LG); Combinatorics (math.CO)
Cite as: arXiv:2609.25149 [cs.LG]
(or arXiv:2609.25149v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.25149
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tianfeng Chen [view email] [v1] Mon, 21 Sep 2026 07:24:38 UTC (896 KB)
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