Coloring Big Graphs with AlphaGoZero [article]

Jiayi Huang, Mostofa Patwary, Gregory Diamos
2019 arXiv   pre-print
We show that recent innovations in deep reinforcement learning can effectively color very large graphs – a well-known NP-hard problem with clear commercial applications. Because the Monte Carlo Tree Search with Upper Confidence Bound algorithm used in AlphaGoZero can improve the performance of a given heuristic, our approach allows deep neural networks trained using high performance computing (HPC) technologies to transform computation into improved heuristics with zero prior knowledge. Key to
more » ... ur approach is the introduction of a novel deep neural network architecture (FastColorNet) that has access to the full graph context and requires O(V) time and space to color a graph with V vertices, which enables scaling to very large graphs that arise in real applications like parallel computing, compilers, numerical solvers, and design automation, among others. As a result, we are able to learn new state of the art heuristics for graph coloring.
arXiv:1902.10162v3 fatcat:vr625w5fgbf4loln2za3nb4hqm