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A case of limit behaviour of vertex degrees in conditional configuration graphs

Authors: Yury Pavlov;

A case of limit behaviour of vertex degrees in conditional configuration graphs

Abstract

We consider configuration graphs with N vertices. The degrees of the verticesare independent identically distributed random variables according to power-lawdistribution with positive parameter . They are equal to the number of vertex’ssemiedges that are numbered in an arbitrary order. The graph is constructed byjoining all of the semiedges pairwise equiprobably to form edges. Such models canbe used for describing different communication networks and Internet topology. Westudy the subset of random graphs under the condition that the sum of vertexdegrees is known and it is equal to n. The properties of the graph depend on thebehaviour of the parameter . We assume that is a random variable followinguniform distribution on the interval [a; b]; 0 < a < b < 1. Let (N) and r be themaximum vertex degree and the number of vertices with a given degree r. Limitdistributions of these random variables as N; n ! 1 in such a way that n=N ! 1were known only if a 6 1. In the paper we proved limit theorems for (N) and rin the case a > 1

Keywords

Science, Q, limit theorems., configuration random graph, vertex degree

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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