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DOI: 10.4230/LIPIcs.CP.2025.22

Learning to Bound for Maximum Common Subgraph Algorithms

Authors: Buddhi W. Kothalawala, Henning Koehler, and Qing Wang

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

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.

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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)

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@InProceedings{kothalawala_et_al:LIPIcs.CP.2025.22,
  author =	{Kothalawala, Buddhi W. and Koehler, Henning and Wang, Qing},
  title =	{{Learning to Bound for Maximum Common Subgraph Algorithms}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{22:1--22:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.22},
  URN =		{urn:nbn:de:0030-drops-238837},
  doi =		{10.4230/LIPIcs.CP.2025.22},
  annote =	{Keywords: Combinatorial Search, Branch and Bound, Graph Theory}
}

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Learning to Bound for Maximum Common Subgraph Algorithms

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