Bounded expansion in models of webgraphs

We study the bounded expansion of several models of web graphs. We show that various deterministic graph models for large complex networks have constant bounded expansion.We study two random models of webgraphs, showing that the model of Bonato has not bounded expansion, and we conjecture that the classical model of Barabási may have also not bounded expansion

Infinite Locally Random Graphs

Motivated by copying models of the web graph, Bonato and Janssen [Bonato and Janssen 03] introduced the following simple construction: given a graph G, for each vertex x and each subset X of its closed neighborhood, add a new vertex y whose neighbors are exactly X. Iterating this construction yields a limit graph ↑G. Bonato and Janssen claimed that the limit graph is independent of G, and it is known as the infinite locally random graph. We show that this picture is incorrect: there are in fact infinitely many isomorphism classes of limit graph, and we give a classification. We also consider the inexhaustibility of these graphs

Infinite limits of copying models of the web graph

Abstract. Several stochastic models were proposed recently to model the dynamic evolution of the web graph. We study the infinite limits of the stochastic processes proposed to model the web graph when time goes to infinity. We prove that deterministic variations of the so-called copying model can lead to several nonisomorphic limits. Some models converge to the infinite random graph R, while the convergence of other models is sensitive to initial conditions or minor changes in the rules of the model. We explain how limits of the copying model of the web graph share several properties with R that seem to reflect known properties of the web graph. 1

Infinite Limits of Copying Models of the Web Graph

Several stochastic models were proposed recently to model the dynamic evolution of the web graph. We study the infinite limits of the stochastic processes proposed to model the web graph when time goes to infinity. We prove that deterministic variations of the so-called copying model can lead to several nonisomorphic limits. Some models converge to the infinite random graph R, while the convergence of other models is sensitive to initial conditions or minor changes in the rules of the model. We explain how limits of the copying model of the web graph share several properties with R that seem to reflect known properties of the web graph