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Learn to solve dominating set problem with GNN and reinforcement learning

Author

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  • Chen, Mujia
  • Liu, Sihao
  • He, Weihua

Abstract

The Dominating Set Problem has a wide range of applications in many industrial areas and the problem has been proven to be NP-hard. The idea using neural networks to solve combinatorial optimization problems has been shown to be effective and time-saving in recent years. Inspired by these studies, to solve the Dominating Set Problem, we train a neural network by Double Deep Q-Networks (DDQN). To better capture the features and structures of the graph, we use a message passing network for the graph representation. We validate our model on random graphs of different sizes, and even on several different lattice graphs, which show our model is effective.

Suggested Citation

  • Chen, Mujia & Liu, Sihao & He, Weihua, 2024. "Learn to solve dominating set problem with GNN and reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 474(C).
    • Handle: RePEc:eee:apmaco:v:474:y:2024:i:c:s0096300324001899
    DOI: 10.1016/j.amc.2024.128717
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