Anomalous scaling in redirection networks

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Hartle, Harrison, Krapivsky, P. L., Redner, S., Zhang, Yuanzhao
Format: Preprint
Publié: 2026
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866914439335247872
author Hartle, Harrison
Krapivsky, P. L.
Redner, S.
Zhang, Yuanzhao
author_facet Hartle, Harrison
Krapivsky, P. L.
Redner, S.
Zhang, Yuanzhao
contents In networks that grow by isotropic redirection (IR), a new node selects an initial target node uniformly at random and attaches to a randomly chosen neighbor of the target. The emerging networks exhibit leaf proliferation, in which the number of nonleaves scales sublinearly as $N^μ$ and the degree distribution has an algebraic tail with exponent $1+μ$. To understand these mysterious properties, we introduce a class of models with redirection to leaves whenever possible. The resulting networks exhibit qualitatively similar phenomenology to IR networks, but avoid the inherent non-locality of the IR growth rule. These networks admit an analytical description of the leaf degree distribution, from which we extract the exponent $μ$.
format Preprint
id arxiv_https___arxiv_org_abs_2604_01540
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Anomalous scaling in redirection networks
Hartle, Harrison
Krapivsky, P. L.
Redner, S.
Zhang, Yuanzhao
Statistical Mechanics
Social and Information Networks
Probability
Physics and Society
In networks that grow by isotropic redirection (IR), a new node selects an initial target node uniformly at random and attaches to a randomly chosen neighbor of the target. The emerging networks exhibit leaf proliferation, in which the number of nonleaves scales sublinearly as $N^μ$ and the degree distribution has an algebraic tail with exponent $1+μ$. To understand these mysterious properties, we introduce a class of models with redirection to leaves whenever possible. The resulting networks exhibit qualitatively similar phenomenology to IR networks, but avoid the inherent non-locality of the IR growth rule. These networks admit an analytical description of the leaf degree distribution, from which we extract the exponent $μ$.
title Anomalous scaling in redirection networks
topic Statistical Mechanics
Social and Information Networks
Probability
Physics and Society
url https://arxiv.org/abs/2604.01540