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Branch-and-bound (BnB) algorithms are widely used to solve combinatorialproblems, and the performance crucially depends on its branching heuristic.Inthis work, we consider a typical problem of maximum common subgraph (MCS), andpropose a branching heuristic inspired from reinforcement learning with a goalof reaching a tree leaf as early as possible to greatly reduce the search treesize.Extensive experiments show that our method is beneficial and outperformscurrent best BnB algorithm for the MCS.

A Learning based Branch and Bound for Maximum Common Subgraph Problems