aiqbench analysis
RidgeCut addresses a key bottleneck in reinforcement learning for graph optimization by integrating domain-specific structural priors to constrain action spaces and improve exploration efficiency. By transforming complex road networks into linear or circular representations, the method leverages transformer-based policies to achieve superior normalized cut performance on both synthetic and real-world traffic graphs. This approach demonstrates how embedding topological knowledge directly into RL frameworks can yield robust generalization across varying graph scales without relying solely on unconstrained learning.
RIDGECUT: Learning Graph Partitioning with Rings and Wedges
arXiv:2505.13986v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances. However, effectively incorporating domain knowledge into RL frameworks for graph partitioning remains challenging, as existing approaches typically rely on unconstrained node-level actions that lead to large action spaces and inefficient exploration. In this paper, we propose RidgeCut, an RL framework that constrains the action space to enforce structure-aware partitioning in the Normalized Cut problem. Using transportation networks as a motivating example, we introduce a novel concept that leverages domain knowledge about urban road topology -- where natural partitions often take the form of concentric rings and radial wedges. By transforming the graph into linear or circular representations, our method enables the use of transformer-based policies and efficient learning via Proximal Policy Optimization. The resulting partitions from RidgeCut are not only aligned with expected spatial layouts but also achieve lower normalized cuts compared to existing methods. Experimental results on synthetic and real-world traffic graphs demonstrate that RidgeCut consistently outperforms existing methods while exhibiting strong inductive generalization across graph sizes. Although motivated by road networks, RidgeCut provides a general mechanism for embedding structural priors into RL frameworks for graph partitioning.
Abstract:Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances. However, effectively incorporating domain knowledge into RL frameworks for graph partitioning remains challenging, as existing approaches typically rely on unconstrained node-level actions that lead to large action spaces and inefficient exploration. In this paper, we propose RidgeCut, an RL framework that constrains the action space to enforce structure-aware partitioning in the Normalized Cut problem. Using transportation networks as a motivating example, we introduce a novel concept that leverages domain knowledge about urban road topology -- where natural partitions often take the form of concentric rings and radial wedges. By transforming the graph into linear or circular representations, our method enables the use of transformer-based policies and efficient learning via Proximal Policy Optimization. The resulting partitions from RidgeCut are not only aligned with expected spatial layouts but also achieve lower normalized cuts compared to existing methods. Experimental results on synthetic and real-world traffic graphs demonstrate that RidgeCut consistently outperforms existing methods while exhibiting strong inductive generalization across graph sizes. Although motivated by road networks, RidgeCut provides a general mechanism for embedding structural priors into RL frameworks for graph partitioning.
Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2505.13986 [math.OC]
(or arXiv:2505.13986v4 [math.OC] for this version)
Full text under the publisher's open licence · 215 words
Related coverage
Reduce ASR inference costs by 75% with NVIDIA MPS on Amazon EC2
AWS · Aug 27, 2026
GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs
arXiv · Jun 12, 2026
Decentralized Gossip Learning and Federated Averaging for Histopathology Image Classification
arXiv · Sep 16, 2026
News
Quantum Computing Report · Jul 25, 2026