arXiv Machine Learning
Preserving Geometric Integrity in Graph Prompting via Measure-Constrained Optimal Transport
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Pair-Centric Graph Rewiring for Over-Squashing via Optimal Transport-Guided Communication Alignment
arXiv:2608. 10619v1 Announce Type: new Abstract: Message-passing neural networks (MPNNs) often struggle when task-relevant information is distributed across distant regions of a graph, since local propagation must compress remote signals through limited structural interfaces.
arXiv Machine Learning
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Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
arXiv:2608. 06031v1 Announce Type: new Abstract: Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts.
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Beyond Binary: Continuous State Optimization with Graph-Structured Objectives
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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.
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Learning to Optimize: Joint Routing and Flow Allocation on Sparse Non-Euclidean Networks
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arXiv AI
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Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint
The paper presents a reinforcement learning approach to generate graph ensembles that satisfy a hard assortativity constraint, a measure of degree–degree correlation between adjacent nodes. Unlike traditional soft-constraint methods, the learned policy performs degree-preserving rewiring to meet the exact target, reducing generation cost by at least an order of magnitude while preserving over 98% of configurational diversity. Trained on small graphs, the method generalizes to larger sizes and different topologies, allowing precise control over secondary observables such as the clustering coefficient.