Simplifying and Characterizing DAGs and Phylogenetic Networks via Least Common Ancestor Constraints

TL;DR

This paper introduces methods to simplify directed acyclic graphs (DAGs) and phylogenetic networks by focusing on least common ancestor (LCA) vertices, making the structures easier to interpret and more data-supported.

Contribution

It characterizes LCA-relevant DAGs and develops efficient algorithms to transform any DAG into an LCA-relevant form while preserving essential evolutionary information.

Findings

Methods to identify LCAs in DAGs.

Algorithms to convert DAGs into LCA-relevant structures.

Preservation of key structural properties during transformation.

Abstract

Rooted phylogenetic networks, or more generally, directed acyclic graphs (DAGs), are widely used to model species or gene relationships that traditional rooted trees cannot fully capture, especially in the presence of reticulate processes or horizontal gene transfers. Such networks or DAGs are typically inferred from observable data (e.g. genomic sequences of extant species), providing only an estimate of the true evolutionary history. However, these inferred DAGs are often complex and difficult to interpret. In particular, many contain vertices that do not serve as least common ancestors (LCAs) for any subset of the underlying genes or species, thus may lack direct support from the observable data. In contrast, LCA vertices are witnessed by historical traces justifying their existence and thus represent ancestral states substantiated by the data. To reduce unnecessary complexity and eliminate unsupported vertices, we aim to simplify a DAG to retain only LCA vertices while preserving essential evolutionary information. In this paper, we characterize $\mathrm{LCA}$-relevant and $\mathrm{lca}$-relevant DAGs, defined as those in which every vertex serves as an LCA (or unique LCA) for some subset of taxa. We introduce methods to identify LCAs in DAGs and efficiently transform any DAG into an $\mathrm{LCA}$-relevant or $\mathrm{lca}$-relevant one while preserving key structural properties of the original DAG or network. This transformation is achieved using a simple operator ``$\ominus$'' that mimics vertex suppression.