A model for social networks
AbstractSocial networks are organized into communities with dense internal connections, giving rise to high values of the clustering coefficient. In addition, these networks have been observed to be assortative, i.e., highly connected vertices tend to connect to other highly connected vertices, and have broad degree distributions. We present a model for an undirected growing network which reproduces these characteristics, with the aim of producing efficiently very large networks to be used as platforms for studying sociodynamic phenomena. The communities arise from a mixture of random attachment and implicit preferential attachment. The structural properties of the model are studied analytically and numerically, using the k-clique method for quantifying the communities.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 371 (2006)
Issue (Month): 2 ()
Contact details of provider:
Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Social networks; Community structure; Complex networks; Small world;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Luthi, Leslie & Pestelacci, Enea & Tomassini, Marco, 2008. "Cooperation and community structure in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 955-966.
- Ikeda, Nobutoshi, 2010. "Impact of initial lattice structures on networks generated by traces of random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3336-3347.
- Tomassini, Marco & Luthi, Leslie, 2007. "Empirical analysis of the evolution of a scientific collaboration network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 750-764.
- Michael Koenig, 2012. "The Formation of Networks with Local Spillovers and Limited Observability," Discussion Papers 11-004, Stanford Institute for Economic Policy Research.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.