Class of correlated random networks with hidden variables
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Abstract
We study a class models of correlated random networks in which vertices are characterized by \textit{hidden variables} controlling the establishment of edges between pairs of vertices. We find analytical expressions for the main topological properties of these models as a function of the distribution of hidden variables and the probability of connecting vertices. The expressions obtained are checked by means of numerical simulations in a particular example. The general model is extended to describe a practical algorithm to generate random networks with an \textit{a priori} specified correlation structure. We also present an extension of the class, to map non-equilibrium growing networks to networks with hidden variables that represent the time at which each vertex was introduced in the system.Keywords
All Related Versions
- Version 1, 2003-06-03, ArXiv
- Published version: Physical Review E, 68 (3), 036112.
This publication has 3 references indexed in Scilit:
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- Modern Graph TheoryJournal of the American Statistical Association, 2000