Elsevier

Theoretical Computer Science

Volume 551, 25 September 2014, Pages 22-38
Theoretical Computer Science

Fitting truncated geometric distributions in large scale real world networks

https://doi.org/10.1016/j.tcs.2014.05.003Get rights and content
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Abstract

Degree distribution of nodes, especially a power-law degree distribution, has been regarded as one of the most significant structural characteristics of social and information networks. However it is observed here that for many large scale real world networks, the power-law does not fit properly because of the presence of large fluctuations and sparsity in upper and lower tails of the distribution. Here we have proposed to fit the truncated geometric distribution on three distinct and non-overlapping parts of the degree frequency table. Extensive experiments on twenty three (23) real world networks revealed that the proposed model fitted better than the power-law and other distributions.

Keywords

Social networks
Natural computing
Power-law distributions
Heavy-tailed distributions
Maximum likelihood
Truncated geometric distribution

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