Hugging Face Trending Papers
Dual-GNN Multilevel Coarsening for Maximum Independent Set
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arXiv Machine Learning
Sep 23
Dual-GNN Multilevel Coarsening for Maximum Independent Set
arXiv:2609.25149v1 Announce Type: new Abstract: Solving large-scale instances of the Traveling Salesman Problem (TSP) exactly is computationally expensive. Researchers often employ graph sparsificati...
arXiv Machine Learning
Aug 4
CoRe-GNN: Multilevel Message passing on Coarsened graphs
arXiv:2608. 02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers.
arXiv Machine Learning
Jun 25
Learning Structural Hardness for Combinatorial Auctions: Instance-Dependent Algorithm Selection via Graph Neural Networks
arXiv:2602. 14772v2 Announce Type: replace Abstract: The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fast greedy heuristics.
Hugging Face Trending Papers
Aug 3
CoRe-GNN: Multilevel Message passing on Coarsened graphs
Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified perspective that exposes their respective limitations.
arXiv AI
Jul 15
Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning
arXiv:2512. 01113v2 Announce Type: replace-cross Abstract: Algorithmic reasoning -- the ability to perform step-by-step logical inference -- is a synthetic benchmark for evaluating multi-step reasoning abilities, designed for graph neural networks and also for transformer models.
arXiv Machine Learning
Jul 27
Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement
arXiv:2607. 21885v1 Announce Type: new Abstract: Coarsening-based training for graph neural networks (GNNs), i.