Detectability of hierarchical communities in networks

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Peel, Leto, Schaub, Michael T.
Format: Preprint
Veröffentlicht: 2020
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866911007766478848
author Peel, Leto
Schaub, Michael T.
author_facet Peel, Leto
Schaub, Michael T.
contents We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analysed in detail and a phase transition has been identified below which the partition cannot be detected. Here we show that, in the hierarchical setting, there exist additional phases in which the presence of multiple consistent partitions can either help or hinder detection. Accordingly, the detectability limit for non-hierarchical partitions typically provides insufficient information about the detectability of the complete hierarchical structure, as we highlight with several constructive examples.
format Preprint
id arxiv_https___arxiv_org_abs_2009_07525
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle Detectability of hierarchical communities in networks
Peel, Leto
Schaub, Michael T.
Social and Information Networks
Machine Learning
Physics and Society
We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analysed in detail and a phase transition has been identified below which the partition cannot be detected. Here we show that, in the hierarchical setting, there exist additional phases in which the presence of multiple consistent partitions can either help or hinder detection. Accordingly, the detectability limit for non-hierarchical partitions typically provides insufficient information about the detectability of the complete hierarchical structure, as we highlight with several constructive examples.
title Detectability of hierarchical communities in networks
topic Social and Information Networks
Machine Learning
Physics and Society
url https://arxiv.org/abs/2009.07525