A Learning Based Branch and Bound for Maximum Common Subgraph Related Problems

Yanli Liu (1) , Chu-Min Li (2, 3) , Hua Jiang (4) , Kun He (5)

1 College of Computer Science [Chengdu]
2 COALA - COntraintes, ALgorithmes et Applications
3 MIS - Modélisation, Information et Systèmes - UR UPJV 4290
4 UCCS - University of Colorado [Colorado Springs]
5 HUST - Huazhong University of Science and Technology [Wuhan]

The performance of a branch-and-bound (BnB) algorithm for maximum common subgraph (MCS) problem and its related problems, like maximum common connected subgraph (MCCS) and induced Subgraph Isomorphism (SI), crucially depends on the branching heuristic. We 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. Experimental results show that the proposed heuristic consistently and significantly improves the current best BnB algorithm for the MCS, MCCS and SI problems. An analysis is carried out to give insight on why and how reinforcement learning is useful in the new branching heuristic.

Domaines: Informatique [cs]

Yanli Liu, Chu-Min Li, Hua Jiang, Kun He. A Learning Based Branch and Bound for Maximum Common Subgraph Related Problems. THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, Feb 2020, New-York, United States. pp.2392-2399. ⟨hal-03636418⟩