Massively scalable inference of level-1 phylogenetic networks

Recent advancements in sequencing technologies have enabled large-scale phylogenomic analyses. While these analyses often rely on phylogenetic trees, increasing evidence suggests that non-treelike evolutionary events, such as hybridization and horizontal gene transfer, are prevalent in the evolutionary histories of many species, and in such cases, tree-based models are insufficient. Phylogenetic networks can capture such complex evolutionary histories, but current methods for accurately inferring them lack scalability. For instance, state-of-the-art model-based approaches are limited to around 30 taxa. Implicit network inference methods like NeighborNet and Consensus Networks are fast but lack biological interpretability. Here, we introduce a novel method called InPhyNet that merges a set of non-overlapping, independently inferred networks into a unified topology, achieving linear scalability while maintaining high accuracy under the multispecies network coalescent model. Our simulation..., , # Massively scalable inference of level-1 phylogenetic networks

All contents of this repository are contained in a single compressed file `data.tar.gz`. This repository contains the simulation study and empirical analysis for InPhyNet, a novel statistical method for inferring large phylogenetic networks. InPhyNet addresses the computational challenges of inferring species networks with hundreds of taxa by using a divide-and-conquer approach that decomposes the problem into smaller, manageable subproblems.

## Overview

Phylogenetic networks are mathematical models that represent evolutionary relationships among species, including both divergence (speciation) and reticulation (hybridization, horizontal gene transfer, introgression) events. Traditional network inference methods become computationally intractable for large datasets (>30 taxa). InPhyNet overcomes this limitation through:

1. **Subset decomposition**: Breaking the full taxon set into smaller, non-overlapping subsets
2. **Co...,