Hot-Get-Richer Network Growth Model

Faisal Nsour*, Hiroki Sayama

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
EditorsRosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, Marta Sales-Pardo
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030653507
Publication statusPublished - 2021
Event9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020 - Madrid, Spain
Duration: 2020 Dec 12020 Dec 3

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


Conference9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020


  • Degree dynamics
  • First-mover advantage
  • Hot-get-richer
  • Network growth
  • Preferential attachment
  • Winner-take-all

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this