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Inferring network mechanisms: the Drosophila melanogaster protein interaction network.

ABSTRACT: Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data subsampling) classified as a duplication-mutation-complementation network over preferential attachment, small-world, and a duplication-mutation mechanism without complementation. Systematic classification, rather than statistical study of specific properties, provides a discriminative approach to understand the design of complex networks.

SUBMITTER: Middendorf M

PROVIDER: S-EPMC552930 | biostudies-literature | 2005 Mar

REPOSITORIES: biostudies-literature

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Inferring network mechanisms: the Drosophila melanogaster protein interaction network.

Middendorf Manuel M Ziv Etay E Wiggins Chris H CH

Proceedings of the National Academy of Sciences of the United States of America 20050222 9

Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data ...[more]

PMID: 15728374

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