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Learning to Bound for Maximum Common Subgraph Algorithms

Authors Buddhi W. Kothalawala , Henning Koehler , Qing Wang

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Subject Classification

ACM Subject Classification
  • Theory of computation → Branch-and-bound
  • Mathematics of computing → Combinatorial optimization
Keywords
  • Combinatorial Search
  • Branch and Bound
  • Graph Theory

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Abstract

Identifying the maximum common subgraph between two graphs is a computationally challenging NP-hard problem. While the McSplit algorithm represents a state-of-the-art approach within a branch-and-bound (BnB) framework, several extensions have been proposed to enhance its vertex pair selection strategy, often utilizing reinforcement learning techniques. Nonetheless, the quality of the upper bound remains a critical factor in accelerating the search process by effectively pruning unpromising branches. This research introduces a novel, more restrictive upper bound derived from a detailed analysis of the McSplit algorithm’s generated partitions. To enhance the effectiveness of this bound, we propose a reinforcement learning approach that strategically directs computational effort towards the most promising regions within the search space.

Cite As Get BibTex

Buddhi W. Kothalawala, Henning Koehler, and Qing Wang. Learning to Bound for Maximum Common Subgraph Algorithms. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 22:1-22:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.CP.2025.22

Author Details

Buddhi W. Kothalawala
  • The Australian National University, Canberra, Australia
Henning Koehler
  • Massey University, Palmerston North, New Zealand
Qing Wang
  • The Australian National University, Canberra, Australia

Funding

This research was supported partially by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (project DP210102273).

Acknowledgements

We are thankful to Dr Muhammad Farhan for helpful discussions and feedback.

Supplementary Materials

References

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