View in EDS

The transsortative structure of networks.

Saved in:
Bibliographic Details
Title: The transsortative structure of networks.
Authors: Ngo, Shin-Chieng1,2 (AUTHOR) xinzengw@usc.edu, Percus, Allon G.2,3 (AUTHOR), Burghardt, Keith2 (AUTHOR) keithab@isi.edu, Lerman, Kristina2 (AUTHOR)
Source: Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences. May2020, Vol. 476 Issue 2237, p1-16. 16p.
Subjects: Illusion (Philosophy), Neighbors, Sensory perception, Topology, Statistics
Abstract: Network topologies can be highly non-trivial, due to the complex underlying behaviours that form them. While past research has shown that some processes on networks may be characterized by local statistics describing nodes and their neighbours, such as degree assortativity, these quantities fail to capture important sources of variation in network structure. We define a property called transsortativity that describes correlations among a node's neighbours. Transsortativity can be systematically varied, independently of the network's degree distribution and assortativity. Moreover, it can significantly impact the spread of contagions as well as the perceptions of neighbours, known as the majority illusion. Our work improves our ability to create and analyse more realistic models of complex networks. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences is the property of Royal Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 143652383
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: The transsortative structure of networks.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ngo%2C+Shin-Chieng%22">Ngo, Shin-Chieng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> xinzengw@usc.edu</i><br /><searchLink fieldCode="AR" term="%22Percus%2C+Allon+G%2E%22">Percus, Allon G.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Burghardt%2C+Keith%22">Burghardt, Keith</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> keithab@isi.edu</i><br /><searchLink fieldCode="AR" term="%22Lerman%2C+Kristina%22">Lerman, Kristina</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+Royal+Society+A%3A+Mathematical%2C+Physical+%26+Engineering+Sciences%22">Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences</searchLink>. May2020, Vol. 476 Issue 2237, p1-16. 16p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Illusion+%28Philosophy%29%22">Illusion (Philosophy)</searchLink><br /><searchLink fieldCode="DE" term="%22Neighbors%22">Neighbors</searchLink><br /><searchLink fieldCode="DE" term="%22Sensory+perception%22">Sensory perception</searchLink><br /><searchLink fieldCode="DE" term="%22Topology%22">Topology</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Network topologies can be highly non-trivial, due to the complex underlying behaviours that form them. While past research has shown that some processes on networks may be characterized by local statistics describing nodes and their neighbours, such as degree assortativity, these quantities fail to capture important sources of variation in network structure. We define a property called transsortativity that describes correlations among a node's neighbours. Transsortativity can be systematically varied, independently of the network's degree distribution and assortativity. Moreover, it can significantly impact the spread of contagions as well as the perceptions of neighbours, known as the majority illusion. Our work improves our ability to create and analyse more realistic models of complex networks. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences is the property of Royal Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=143652383
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1098/rspa.2019.0772
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 1
    Subjects:
      – SubjectFull: Illusion (Philosophy)
        Type: general
      – SubjectFull: Neighbors
        Type: general
      – SubjectFull: Sensory perception
        Type: general
      – SubjectFull: Topology
        Type: general
      – SubjectFull: Statistics
        Type: general
    Titles:
      – TitleFull: The transsortative structure of networks.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ngo, Shin-Chieng
      – PersonEntity:
          Name:
            NameFull: Percus, Allon G.
      – PersonEntity:
          Name:
            NameFull: Burghardt, Keith
      – PersonEntity:
          Name:
            NameFull: Lerman, Kristina
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2020
              Type: published
              Y: 2020
          Identifiers:
            – Type: issn-print
              Value: 13645021
          Numbering:
            – Type: volume
              Value: 476
            – Type: issue
              Value: 2237
          Titles:
            – TitleFull: Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences
              Type: main
ResultId 1