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Quantifying randomness in protein–protein interaction networks of different species: A random matrix approach

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  • Agrawal, Ankit
  • Sarkar, Camellia
  • Dwivedi, Sanjiv K.
  • Dhasmana, Nitesh
  • Jalan, Sarika

Abstract

We analyze protein–protein interaction networks for six different species under the framework of random matrix theory. Nearest neighbor spacing distribution of the eigenvalues of adjacency matrices of the largest connected part of these networks emulate universal Gaussian orthogonal statistics of random matrix theory. We demonstrate that spectral rigidity, which quantifies long range correlations in eigenvalues, for all protein–protein interaction networks follow random matrix prediction up to certain ranges indicating randomness in interactions. After this range, deviation from the universality evinces underlying structural features in network.

Suggested Citation

  • Agrawal, Ankit & Sarkar, Camellia & Dwivedi, Sanjiv K. & Dhasmana, Nitesh & Jalan, Sarika, 2014. "Quantifying randomness in protein–protein interaction networks of different species: A random matrix approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 359-367.
    • Handle: RePEc:eee:phsmap:v:404:y:2014:i:c:p:359-367
    DOI: 10.1016/j.physa.2013.12.005
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    Cited by:

    1. Matharoo, Gurpreet S. & Hashmi, Javeria A., 2020. "Spontaneous back-pain alters randomness in functional connections in large scale brain networks: A random matrix perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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