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Communication Dans Un Congrès Année : 2020

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

Yanli Liu
  • Fonction : Auteur
Hua Jiang
  • Fonction : Auteur
  • PersonId : 1266044

Résumé

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.
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Dates et versions

hal-04322004 , version 1 (06-12-2023)

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  • HAL Id : hal-04322004 , version 1

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Yanli Liu, Chu-Min Li, Hua Jiang. A Learning Based Branch and Bound for Maximum Common Subgraph Related Problems. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), Feb 2020, New York, United States. pp.2392-2399. ⟨hal-04322004⟩
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