Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search
Machine Learning
2023-11-20 v1
Abstract
Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing graph sparsifiers by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node to be added as output. This neural network is then used in a Monte Carlo search to compute a sparsifier. The proposed method consistently outperforms several standard approximation algorithms on different types of graphs and often finds the optimal solution.
Cite
@article{arxiv.2311.10316,
title = {Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search},
author = {Alvin Chiu and Mithun Ghosh and Reyan Ahmed and Kwang-Sung Jun and Stephen Kobourov and Michael T. Goodrich},
journal= {arXiv preprint arXiv:2311.10316},
year = {2023}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2305.00535