arXiv Machine Learning
GATNextHop: A GAT for Shortest Path Routing with Cross-Topology Generalization
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arXiv Machine Learning
5d ago
Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks
The paper introduces TELGEN, a traffic engineering algorithm that uses graph neural networks to predict an optimal TE algorithm rather than a direct solution. TELGEN generalizes across diverse network topologies and traffic patterns, achieving less than a 3% optimality gap on networks up to 5,000 nodes and 3.6 million links, while reducing solving time by up to 84% and training time by up to 79.6% compared to existing methods.
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 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 AI
Jul 22
GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
arXiv Machine Learning
Aug 4
Learning Graph-Indexed Trajectory Patterns for Stochastic On-Time Arrival Routing
arXiv:2508. 17218v4 Announce Type: replace Abstract: Correlated link travel times create decision-relevant patterns in partial route histories.
Hugging Face Trending Papers
Jul 21
GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.