Solving the Tree Containment Problem Using Graph Neural Networks

• URL: http://arxiv.org/abs/2404.09812v2
• Date: Thu, 13 Jun 2024 09:20:40 GMT
• Title: Solving the Tree Containment Problem Using Graph Neural Networks
• Authors: Arkadiy Dushatskiy, Esther Julien, Leen Stougie, Leo van Iersel,
• Abstract summary: Tree Containment is a problem in phylogenetics useful for verifying a proposed phylogenetic network. We propose to solve it approximately using Graph Neural Networks. Our algorithm demonstrates an accuracy of over $95%$ in solving the tree containment problem on instances with up to 100 leaves.
• Score: 0.6081917632687639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tree Containment is a fundamental problem in phylogenetics useful for verifying a proposed phylogenetic network, representing the evolutionary history of certain species. Tree Containment asks whether the given phylogenetic tree (for instance, constructed from a DNA fragment showing tree-like evolution) is contained in the given phylogenetic network. In the general case, this is an NP-complete problem. We propose to solve it approximately using Graph Neural Networks. In particular, we propose to combine the given network and the tree and apply a Graph Neural Network to this network-tree graph. This way, we achieve the capability of solving the tree containment instances representing a larger number of species than the instances contained in the training dataset (i.e., our algorithm has the inductive learning ability). Our algorithm demonstrates an accuracy of over $95\%$ in solving the tree containment problem on instances with up to 100 leaves.

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