Author: Kejzar Natasa Nikoloski Zoran Batagelj Vladimir
Publisher: Routledge Ltd
ISSN: 0022-250X
Source: Journal of Mathematical Sociology, Vol.32, Iss.2, 2008-04, pp. : 85-109
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
A unifying framework - probabilistic inductive classes of graphs (PICGs) - is defined by imposing a probability space on the rules and their left elements from the standard notion of inductive class of graphs. The rules can model the processes creating real-world social networks, such as spread of knowledge, dynamics of acquaintanceships or sexual contacts, and emergence of clusters. We demonstrate the characteristics of PICGs by casting some well-known models of growing networks in this framework. Results regarding expected size and order are derived. For PICG models of connected and 2-connected graphs order, size and asymptotic degree distribution are presented. The approaches used represent analytic alternative to computer simulation, which is mostly used to obtain the properties of evolving graphs.
Related content
LITERARY CHINESE BY THE INDUCTIVE METHOD
Bulletin of the School of Oriental and African Studies, Vol. 9, Iss. 4, 1939-02 ,pp. :
Metascience, Vol. 14, Iss. 2, 2005-08 ,pp. :
Scientometrics in the Context of Probabilistic Philosophy
By Gurjeva Lyubov Wouters Paul
Scientometrics, Vol. 52, Iss. 2, 2001-10 ,pp. :
BETWEENNESS CENTRALIZATION FOR BIPARTITE GRAPHS
Journal of Mathematical Sociology, Vol. 29, Iss. 1, 2004-12 ,pp. :