  Maximum Common induced Subgraph (MCS) is an important NP-hard problem with
wide real-world applications. Branch-and-Bound (BnB) is the basis of a class of
efficient algorithms for MCS, consisting in successively selecting vertices to
match and pruning when it is discovered that a solution better than the best
solution found so far does not exist. The method of selecting the vertices to
match is essential for the performance of BnB. In this paper, we propose a new
value function and a hybrid selection strategy used in reinforcement learning
to define a new vertex selection method, and propose a new BnB algorithm,
called McSplitDAL, for MCS. Extensive experiments show that McSplitDAL
significantly improves the current best BnB algorithms, McSplit+LL and
McSplit+RL. An empirical analysis is also performed to illustrate why the new
value function and the hybrid selection strategy are effective.
