Detection of the distribution and parameter estimation for the departing connectivity in biological networks

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2014
Odunsi, Omolola Dorcas
The connectivity density is one of the characteristics features in the topology of the network. This density describes the total number of the in-degree and out-degree of a node in a system. In a network, the in-degree or arriving connectivity represents the number of links coming to a target gene and the out-degree or departing connectivity stands for the number of links departing from the target gene. For biological networks, the density of the in-degree is represented by the exponential distribution and the distribution of the out-degree is generally referred by the power-law density. But the truncated power-law, generalized pareto law, stretched exponential, geometric and combination of these densities can be also strong alternatives for the out-degree densities which satisfy the centrality and small-world properties without the scale-free feature of the biological networks. In this study we investigate the out-degree of the biological network within the Pearson curves. For the detection, we use both real and simulated datasets and compute the moments of the data for the plausible classification of the density. Moreover we investigate the application of the three-moment chi-square and four-moment F approximations for the out-degree distributions.

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Citation Formats
O. D. Odunsi, “Detection of the distribution and parameter estimation for the departing connectivity in biological networks,” M.S. - Master of Science, Middle East Technical University, 2014.