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.
By Fangtong Zhou, Xiaorui Liu, Ruozhou Yu, Guoliang Xue
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.
By Yimeng Min, Carla P. Gomes
arXiv:2606. 19185v1 Announce Type: new Abstract: The Traveling Salesman Problem (TSP) is a cornerstone of combinatorial optimization and arises in many practical scenarios.
By Bolin Shen, Ziwei Huang, Zhiguang Cao, Yushun Dong
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
By Alessandro Scalese, Santhanakrishnan Narayanan, Constantinos Antoniou
arXiv:2508. 17218v4 Announce Type: replace Abstract: Correlated link travel times create decision-relevant patterns in partial route histories.
By Yuanhang Wang, Xing Wei, Duoxiang Zhao, Zezhou Zhang, Hao Qin, Yuqi Ouyang
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.
arXiv:2608.28627v1 Announce Type: new
Abstract: Designing high-performance tactical wireless networks under realistic operational constraints gives rise to challenging combinatorial optimization prob...
By Wissem Ahmed Zaid, Alain Hertz, Defeng Liu
arXiv:2607. 23467v1 Announce Type: new Abstract: We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service.
By Haomiao Sun, Fang He, Congyuan Ji, Xindi Tang
arXiv:2607. 09372v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly.
By Samra Sana, Giorgio Mantica, Saul Imbrici
arXiv:2602. 00488v3 Announce Type: replace Abstract: Solving large-scale capacitated vehicle routing problems (CVRP) is hindered by the high complexity of classical heuristics and the limited generalization of neural solvers.
By Dongbin Jiao, Zisheng Chen, Xianyi Wang, Jintao Shi, Shengcai Liu, Shi Yan
arXiv:2602.09716v2 Announce Type: replace
Abstract: Computing the importance of nodes in networks is a long-standing fundamental problem that has driven extensive study of various centrality measures...
By Justin Dachille, Aurora Rossi, Sunil Kumar Maurya, Frederik Mallmann-Trenn, Xin Liu, Fr\'ed\'eric Giroire, Tsuyoshi Murata, Emanuele Natale
The paper introduces a deep architecture that jointly optimizes cost functions and a route-ranking model to accommodate diverse user preferences in route planning. It first generates a complete set of Pareto‑optimal routes using a multi‑objective Dijkstra algorithm, then employs a neural network that emulates shortest‑path search and ranking in an end‑to‑end differentiable framework. A novel loss function treats route preference as a constrained optimization problem, allowing a single objective to be optimized while other attributes remain constrained, and experiments on real‑world data show significant improvements over existing methods.
By Rui Zhao, Chao Chen, Longfei Xu, Chenguang Ji, Hengbin Cui, Kaikui Liu, Xiaolong Li