Solving the tree containment problem using graph neural networks

Tree containment is a fundamental problem in phylogenetics useful for verifying a pro- posed 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.