Hot-Get-Richer Network Growth Model

Faisal Nsour, Hiroki Sayama

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Under preferential attachment (PA) network growth models late arrivals are at a disadvantage with regard to their final degrees. Previous extensions of PA have addressed this deficiency by either adding the notion of node fitness to PA, usually drawn from some fitness score distributions, or by using fitness alone to control attachment. Here we introduce a new dynamical approach to address late arrivals by adding a recent-degree-change bias to PA so that nodes with higher relative degree change in temporal proximity to an arriving node get an attachment probability boost. In other words, if PA describes a rich-get-richer mechanism, and fitness-based approaches describe good-get-richer mechanisms, then our model can be characterized as a hot-get-richer mechanism, where hotness is determined by the rate of degree change over some recent past. The proposed model produces much later high-ranking nodes than the PA model and, under certain parameters, produces networks with structure similar to PA networks.

本文言語English
ホスト出版物のタイトルComplex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
編集者Rosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, Marta Sales-Pardo
出版社Springer Science and Business Media Deutschland GmbH
ページ532-543
ページ数12
ISBN(印刷版)9783030653507
DOI
出版ステータスPublished - 2021
イベント9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020 - Madrid, Spain
継続期間: 2020 12 12020 12 3

出版物シリーズ

名前Studies in Computational Intelligence
944
ISSN(印刷版)1860-949X
ISSN(電子版)1860-9503

Conference

Conference9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020
CountrySpain
CityMadrid
Period20/12/120/12/3

ASJC Scopus subject areas

  • Artificial Intelligence

引用スタイル