Graph Coarsening

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Dual-GNN Multilevel Coarsening for Maximum Independent Set
Sep 21, 2026Tianfeng Chen, Xianyue LiGraph Coarsening Combinatorial Optimization
School of Mathematics and Statistics Lanzhou University
Geometric Flow enhanced Graph Coarsening
Sep 14, 2026Chaoqun Fei, Guoxuan Li, Tinglve Zhou +2Graph Neural Networks Graph Coarsening
GNN-Guided Graph Coarsening and Adaptive QUBO Penalties for the Capacitated Vehicle Routing Problem with Time Windows on a Quantum Annealer
Sep 4, 2026Youssef Kamel Rezk, Paweł GoraQuantum Annealing Graph Coarsening
Alamein International University, Alamein, Egypt · Jagiellonian University, Kraków, Poland · Fundacja Quantum AI, Warsaw, Poland
CoRe-GNN: Multilevel Message passing on Coarsened graphs
Aug 3, 2026Antonin Joly, Nicolas Keriven, Aline RoumyGraph Neural Networks Graph Coarsening
CNRS, IRISA, Rennes, FRANCE · INRIA, Rennes, FRANCE
Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
Jul 30, 2026Robert Jankowski, Pedro Almagro-Blanco, Marián Boguñá +2Zero-Shot Learning Graph Neural Networks
TU Delft · University of Seville · University of Barcelona +2
Does Graph Compression Preserve Signal Propagation?
Jul 25, 2026Kawshik Banerjee, Khaled Mohammed SaifuddinGraph Signal Processing Graph Coarsening
School of Computing, Southern Illinois University, Carbondale, IL, USA
Efficient Recommendations via Graph Coarsening and Label Propagation
Jul 24, 2026Alessandro Sbandi, Federico Siciliano, Fabrizio SilvestriGraph Coarsening Recommender Systems
Sapienza University of Rome, Rome, Italy · TIM S.p.A.
Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement
Jul 24, 2026Guoming Li, Jian Yang, Xukun Wang +3Graph Neural Networks Graph Coarsening
Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China · School of Information, Renmin University of China, Beijing, China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China +2
SEMIR: Topology-Preserving Graph Minors for Thin-Structure Segmentation
Jun 22, 2026Luke James Miller, Yugyung LeeImage Segmentation Graph Coarsening
University of Missouri-Kansas City, Kansas City MO 64110, USA
Hierarchical Pooling for Sheaf Neural Networks
Jun 18, 2026Dionisia Naddeo, Carlo Abate, Pietro Liò +2Graph Neural Networks Graph Coarsening
UiT The Arctic University of Norway · UiT The Arctic University of Norway NORCE Norwegian Research Centre AS
Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark
Jun 10, 2026Mateusz Stolarski, Michał Czuba, Piotr Bielak +1Graph Coarsening Graph Sparsification
Wrocław University of Science and Technology, Wrocław, Poland
Rethinking Efficient Graph Coarsening via a Non-Selfishness Principle
May 13, 2026Xu Bai, Bin Lu, Kun Zhang +4Graph Coarsening
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China · School of Environment Science and Engineering, Shanghai Jiao Tong University, Shanghai, China · School of Artificial Intelligence, Nanchang University, Nanchang, China +2
SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation
May 12, 2026Luke James Miller, Yugyung LeeGraph Structure Learning Image Segmentation
Department of Computing, Analytics and Mathematics; University of Missouri-Kansas City, Kansas City, United States.
Diversity Curves for Graph Representation Learning
May 7, 2026Katharina Limbeck, Nadja Häusermann, Martin Carrasco +2Graph Representation Learning Graph Coarsening
1Helmholtz Munich · 2Technical University of Munich · 5Université de Fribourg +2
NervePool: A Simplicial Pooling Layer
May 10, 2023Sarah McGuire Scullen, Ernst Röell, Elizabeth Munch +2Graph Coarsening Graph Pooling
Pacific Northwest National Lab, USA · Helmholtz Munich and Technical University of Munich, Germany · Michigan State University, USA +1

Sep 21, 2026, cs.LGJ/K move · Enter open · S save

Dual-GNN Multilevel Coarsening for Maximum Independent Set

Tianfeng Chen, Xianyue Li

School of Mathematics and Statistics Lanzhou University

Graph Coarsening Combinatorial Optimization

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The maximum independent set (MIS) problem is a fundamental NP-hard combinatorial optimization problem with applications in scheduling, resource allocation, and network analysis. Exact solvers can provide high-quality solutions or optimality certificates, but their computational cost grows rapidly with graph size, while hand-crafted heuristics improve scalability at the expense of guarantees. Learning-based methods offer an alternative by exploiting structural patterns across graph instances, yet directly predicting independent sets can make global coordination difficult on large graphs. We instead use learning to guide multilevel graph coarsening while retaining combinatorial search for final decision making. Our Dual-GNN Multilevel Coarsening framework uses a Partition GNN to score candidate contractions and a Representative GNN to select top-k local independent-set states for each final cluster. Experiments on Erdős--Rényi graphs with up to 2,000 vertices demonstrate a favorable quality--runtime trade-off. On 500-vertex instances with certified optima, our method achieves an average independent-set size of 19.20, corresponding to 99.5% of the optimal value of 19.30, while reducing the mean wall-clock time from 643.57 seconds for exact solving to 3.41 seconds, yielding an approximately 189

\times

speedup. On larger graphs with 1,000 and 2,000 vertices, our method achieves the best mean solution quality among all evaluated methods. Moreover, although trained only on Erdős--Rényi graphs with edge probability

p=0.35

, the learned coarsening policy generalizes effectively across both unseen graph densities and structurally different graph families.

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