Branch-and-bound (BnB) algorithms are widely used to solve combinatorial problems, and the performance crucially depends on its branching heuristic.In this work, we consider a typical problem of maximum common subgraph (MCS), and propose a branching heuristic inspired from reinforcement learning with a goal of reaching a tree leaf as early as possible to greatly reduce the search tree size.Extensive experiments show that our method is beneficial and outperforms current best BnB algorithm for the MCS.
@article{arxiv.1905.05840,
title = {A Learning based Branch and Bound for Maximum Common Subgraph Problems},
author = {Yan-li Liu and Chu-min Li and Hua Jiang and Kun He},
journal= {arXiv preprint arXiv:1905.05840},
year = {2019}
}