Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set

3 February 2023

Papers citing "Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set"

6 / 6 papers shown

Title
A Short Review on Novel Approaches for Maximum Clique Problem: from Classical algorithms to Graph Neural Networks and Quantum algorithms Raffaele Marino L. Buffoni Bogdan Zavalnij GNN 40 5 0 13 Mar 2024
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems Yuma Ichikawa Hiroaki Iwashita CLL 21 1 0 03 Feb 2024
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning Redwan Ahmed Rizvee Raheeb Hassan Md. Mosaddek Khan 18 0 0 27 Nov 2023
Controlling Continuous Relaxation for Combinatorial Optimization Yuma Ichikawa 32 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 21 3 0 05 Jun 2023
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 174 1,106 0 27 Apr 2021