Analysis of a network's asymptotic behavior via its structure involving its strongly connected components

Research output: Contribution to JournalArticleAcademicpeer-review

122 Downloads (Pure)

Abstract

In this paper, it is addressed how network structure can be related to asymptotic network behavior. If such a relation is studied, that usually concerns only strongly connected networks and only linear functions describing the dynamics. In this paper, both conditions are generalized. A couple of general theorems is presented that relates asymptotic behavior of a network to the network's structure characteristics. The network structure characteristics, on the one hand, concern the network's strongly connected components and their mutual connections; this generalizes the condition of being strongly connected to a very general condition. On the other hand, the network structure characteristics considered generalize from linear functions to functions that are normalized, monotonic, and scalar-free, so that many nonlinear functions are also covered. Thus, the contributed theorems generalize the existing theorems on the relation between network structure and asymptotic network behavior addressing only specific cases such as acyclic networks, fully, and strongly connected networks, and theorems addressing only linear functions. This paper was invited as an extended (by more than 45%) version of a Complex Networks'18 conference paper. In the discussion section, the differences are explained in more detail.

Original languageEnglish
Pages (from-to)S82-S109
Number of pages28
JournalNetwork Science
Volume8
Issue numberS1
Early online date1 Oct 2019
DOIs
Publication statusPublished - Jul 2020

Bibliographical note

Special issue on Complex Networks 2018.

Keywords

  • Action Editors:
  • Hocine Cherifi
  • Luis Rocha
  • Stanley Wasserman

Fingerprint

Dive into the research topics of 'Analysis of a network's asymptotic behavior via its structure involving its strongly connected components'. Together they form a unique fingerprint.

Cite this