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Structural identification of large statistically distributed vertex degree

Authors: V. N. Zadorozhnyi; E. B. Yudin;

Structural identification of large statistically distributed vertex degree

Abstract

For the modeling of large networks a random graph is proposed, the generation of which is based on the use of nonlinear preferential attachment rule. The analytical solution for the vertex degree distribution is given as well as the method for the calibration of graph generator according to the node degree distributions in real networks. Possibilities of structural identification are demonstrated using the proposed graph for a network of actors acted the same films, the Internet, networks of roads, networks of complementary goods in online store.

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Powered by OpenAIRE graph
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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!
2
Average
Average
Average
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