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Probabilistic inductive classes of graphs
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Probabilistic inductive classes of graphs

Author: Nataša Kejžar; Zoran Nikoloski; Vladimir Batagelj
Edition/Format:   Article : EnglishView all editions and formats
Publication:The Journal of mathematical sociology, Vol. 32 (2008), str. 85-109
Database:WorldCat
Summary:
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 theprocesses 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  Read more...
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Document Type: Article
All Authors / Contributors: Nataša Kejžar; Zoran Nikoloski; Vladimir Batagelj
ISSN:0022-250X
OCLC Number: 442770606
Notes: Prispevek v tisku, številka revije še ni znana.
Description: str. 85-109.
Responsibility: Nataša Kejžar, Zoran Nikoloski, Vladimir Batagelj.

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 theprocesses 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.

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