  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.
