The weighted total cophenetic index: A novel balance index for phylogenetic networks

TL;DR

This paper introduces a new weighted total cophenetic index for phylogenetic networks, extending a tree balance measure to accommodate reticulate evolutionary events, with efficient computation and desirable mathematical properties.

Contribution

It proposes the first generalized balance index for phylogenetic networks, analyzing its extremal properties and comparing it to existing indices.

Findings

Index can be computed efficiently.

It satisfies locality and recursiveness.

Analysis of maximum and minimum values within level-1 networks.

Abstract

Phylogenetic networks play an important role in evolutionary biology as, other than phylogenetic trees, they can be used to accommodate reticulate evolutionary events such as horizontal gene transfer and hybridization. Recent research has provided a lot of progress concerning the reconstruction of such networks from data as well as insight into their graph theoretical properties. However, methods and tools to quantify structural properties of networks or differences between them are still very limited. For example, for phylogenetic trees, it is common to use balance indices to draw conclusions concerning the underlying evolutionary model, and more than twenty such indices have been proposed and are used for different purposes. One of the most frequently used balance index for trees is the so-called total cophenetic index, which has several mathematically and biologically desirable properties. For networks, on the other hand, balance indices are to-date still scarce. In this contribution, we introduce the \textit{weighted} total cophenetic index as a generalization of the total cophenetic index for trees to make it applicable to general phylogenetic networks. As we shall see, this index can be determined efficiently and behaves in a mathematical sound way, i.e., it satisfies so-called locality and recursiveness conditions. In addition, we analyze its extremal properties and, in particular, we investigate its maxima and minima as well as the structure of networks that achieve these values within the space of so-called level-$1$ networks. We finally briefly compare this novel index to the two other network balance indices available so-far.