Binary normal networks without near reticulations can be reconstructed from their rooted triples
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Updated: Nov 9, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Andrew Francis1, Charles Semple2
1School of Mathematics and Statistics, University of New South Wales, Sydney, Australia.
Abstract:
Normal networks are an important class of phylogenetic networks that have compelling mathematical properties which align with intuition about inference from genetic data. While tools enabling widespread use of phylogenetic networks in the biological literature are still under mathematical, statistical, and computational development, many such results are being assembled, and in particular for normal phylogenetic networks. For instance, it has been shown that binary normal networks can be reconstructed from the sets of three- and four-leaf rooted phylogenetic trees that they display. It is also known that one can reconstruct particular subclasses of normal networks from just the displayed rooted triples. This applies, for instance, to rooted binary phylogenetic trees and to binary level-1 normal networks. In this paper we address the question of how much of the class of binary normal networks can be reconstructed from just the rooted triples that they display. We find that all except those with substructures that we call "near-sibling reticulations" and "near-stack reticulations" can be reconstructed just from their rooted triples. This goes some way to answering the natural question of how much information can be extracted from a set of displayed rooted triples, which are arguably the simplest substructure that one may hope for in a phylogenetic object.
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