Analysis and synthesis of a growing network model generating dense scale-free networks via category theory

Taichi Haruna, Yukio Pegio Gunji

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a growing network model that can generate dense scale-free networks with an almost neutral degree−degree correlation and a negative scaling of local clustering coefficient. The model is obtained by modifying an existing model in the literature that can also generate dense scale-free networks but with a different higher-order network structure. The modification is mediated by category theory. Category theory can identify a duality structure hidden in the previous model. The proposed model is built so that the identified duality is preserved. This work is a novel application of category theory for designing a network model focusing on a universal algebraic structure.

Original languageEnglish
Article number22351
JournalScientific reports
Volume10
Issue number1
DOIs
Publication statusPublished - 2020 Dec

ASJC Scopus subject areas

  • General

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