arXiv AI
Jul 14
GES-TSP: Graph Edge Sparsification for TSP
arXiv:2607. 09708v1 Announce Type: new Abstract: Solving large-scale instances of the Traveling Salesman Problem (TSP) exactly is computationally expensive.
Dual-GNN Multilevel Coarsening for Maximum Independent Set
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Hugging Face Trending Papers
Sep 21
Dual-GNN Multilevel Coarsening for Maximum Independent Set
The maximum independent set (MIS) problem is a fundamental NP-hard combinatorial optimization problem with applications in scheduling, resource allocation, and network analysis. Exact solvers can prov...
arXiv Machine Learning
Jun 18
AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network
arXiv:2606. 19185v1 Announce Type: new Abstract: The Traveling Salesman Problem (TSP) is a cornerstone of combinatorial optimization and arises in many practical scenarios.
arXiv Machine Learning
Aug 18
Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines
arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.
arXiv Machine Learning
Jul 22
Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes
arXiv:2607. 18632v1 Announce Type: new Abstract: Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio.
Hugging Face Trending Papers
Jul 21
Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes
Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio. This is particularly relevant to the Traveling Salesman Problem (TSP), where solver performance is strongly instance-dependent.
arXiv AI
Jul 7
Graph Neural Networks are Heuristics
arXiv:2601. 13465v4 Announce Type: replace Abstract: Graph neural networks are usually treated as auxiliaries for combinatorial optimization: they imitate algorithms, guide search, or supply scores to classical procedures.
arXiv AI
Jul 22
On the Effectiveness of Pretraining for Graph Combinatorial Optimization
arXiv:2607. 19072v1 Announce Type: new Abstract: This paper introduces a self-supervised pretraining framework for graph combinatorial optimization specifically designed to address the nature of routing problems like the Traveling Salesman Problem.
arXiv AI
Jun 2
Learning to Reduce Search Space for Generalizable Neural Routing Solver
arXiv:2503. 03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal solutions, thereby reducing reliance on expert knowledge for algorithm design.
arXiv AI
Aug 6
GRALS: GCN-Guided Redundancy-Aware Local Search for Minimum Vertex Cover
arXiv:2503. 06396v2 Announce Type: replace Abstract: The minimum vertex cover (MVC) problem seeks to identify the smallest set of vertices that cover all edges in an undirected graph.
arXiv Machine Learning
Sep 10
Rethinking One-Shot Federated Graph Learning: Training-Free Statistical Estimation
arXiv:2609.06154v1 Announce Type: new Abstract: One-shot federated graph learning generally aims to train Graph Neural Networks (GNNs) across clients with disconnected subgraphs in a single communica...
arXiv AI
Aug 18
Geometric Self-Supervised Pre-training for Neural Combinatorial Optimization
arXiv:2608. 00270v2 Announce Type: replace Abstract: Neural Combinatorial Optimization (NCO) techniques have emerged as a highly efficient alternative to traditional exact algorithms for solving routing problems such as the Traveling Salesman Problem (TSP).