Structural information of phylogenetic tree topologies plays an important
role in phylogenetic inference. However, finding appropriate topological
structures for specific phylogenetic inference tasks often requires significant
design effort and domain expertise. In this paper, we propose a novel
structural representation method for phylogenetic inference based on learnable
topological features. By combining the raw node features that minimize the
Dirichlet energy with modern graph representation learning techniques, our
learnable topological features can provide efficient structural information of
phylogenetic trees that automatically adapts to different downstream tasks
without requiring domain expertise. We demonstrate the effectiveness and
efficiency of our method on a simulated data tree probability estimation task
and a benchmark of challenging real data variational Bayesian phylogenetic
inference problems.

本文提出基于可学习拓扑特征的系统发育推断结构表示方法，通过结合最小化狄利克雷能量的原始节点特征和现代图表示学习技术，该方法可提供高效的系统发育树结构信息，可自动适应不同的下游任务。在模拟数据和具有挑战性的实际数据上进行验证和评估。