Graph Coloring with Physics-Inspired Graph Neural Networks

Graph Coloring with Physics-Inspired Graph Neural Networks

3 February 2022

Papers citing "Graph Coloring with Physics-Inspired Graph Neural Networks"

15 / 15 papers shown

Title
Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems Chenchuhui Hu 18 0 0 06 Nov 2024
Graph Neural Networks as Ordering Heuristics for Parallel Graph Coloring Kenneth Langedal Fredrik Manne GNN 16 0 0 09 Aug 2024
Efficient Graph Coloring with Neural Networks: A Physics-Inspired Approach for Large Graphs Lorenzo Colantonio Andrea Cacioppo Federico Scarpati Stefano Giagu GNN 15 4 0 02 Aug 2024
A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks Yaochu Jin Xueming Yan Shiqing Liu Xiangyu Wang 44 3 0 19 Jun 2024
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More Fanchen Bu Hyeonsoo Jo Soo Yong Lee Sungsoo Ahn Kijung Shin 22 3 0 14 May 2024
Message Passing Variational Autoregressive Network for Solving Intractable Ising Models Qunlong Ma Zhi Ma Jinlong Xu Hairui Zhang Ming Gao 21 5 0 09 Apr 2024
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems Yuma Ichikawa Hiroaki Iwashita CLL 10 1 0 03 Feb 2024
Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks Daniel Abode Ramoni O. Adeogun Lou Salaün Renato Abreu Thomas Jacobsen Gilberto Berardinelli 11 3 0 13 Dec 2023
Maximum Independent Set: Self-Training through Dynamic Programming Lorenzo Brusca Lars C.P.M. Quaedvlieg Stratis Skoulakis Grigorios G. Chrysos V. Cevher SSL 13 7 0 28 Oct 2023
Are Graph Neural Networks Optimal Approximation Algorithms? Morris Yau Eric Lu Nikolaos Karalias Jessica Xu Stefanie Jegelka 24 1 0 01 Oct 2023
Controlling Continuous Relaxation for Combinatorial Optimization Yuma Ichikawa 17 4 0 29 Sep 2023
$Barriers for the performance of graph neural networks (GNN) in discrete random structures. A comment on~\cite{schuetz2022combinatorial},\cite{angelini2023modern},\cite{schuetz2023reply}$ Barriers for the performance of graph neural networks (GNN) in discrete random structures. A comment on~\cite{schuetz2022combinatorial},\cite{angelini2023modern},\cite{schuetz2023reply} D. Gamarnik 6 3 0 05 Jun 2023
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems M. Schuetz J. K. Brubaker H. Katzgraber 33 2 0 03 Feb 2023
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set M. Schuetz J. K. Brubaker H. Katzgraber 20 10 0 03 Feb 2023
A Graph Neural Network with Negative Message Passing for Graph Coloring Xiangyu Wang Xueming Yan Yaochu Jin 15 7 0 26 Jan 2023