Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Christofer Edling

Christofer Edling

Professor

Christofer Edling

Network bipartivity

Författare

  • P. Holme
  • F. Liljeros
  • Christofer Edling
  • B. J. Kim

Summary, in English

Systems with two types of agents with a preference for heterophilous interaction produce networks that are more or less close to bipartite. We propose two measures quantifying the notion of bipartivity. The two measures-one well known and natural, but computationally intractable, and the other computationally less complex, but also less intuitive-are examined on model networks that continuously interpolate between bipartite graphs and graphs with many odd circuits. We find that the bipartivity measures increase as we tune the control parameters of the test networks to intuitively increase the bipartivity, and thus conclude that the measures are quite relevant. We also measure and discuss the values of our bipartivity measures for empirical social networks (constructed from professional collaborations, Internet communities, and field surveys). Here we find, as expected, that networks arising from romantic online interaction have high, and professional collaboration networks have low, bipartivity values. In some other cases, probably due to low average degree of the network, the bipartivity measures cannot distinguish between romantic and friendship oriented interaction.

Publiceringsår

2003

Språk

Engelska

Publikation/Tidskrift/Serie

Physical Review E

Volym

68

Dokumenttyp

Artikel i tidskrift

Förlag

American Physical Society

Ämne

  • Sociology (excluding Social Work, Social Psychology and Social Anthropology)

Nyckelord

  • sociologi
  • optimization
  • ising-model
  • sociology
  • statistical-mechanics
  • collective behavior
  • complex networks

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 1063-651X