title: 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.
authors.affiliation: Department of Physics, College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA.
authors.forename: Manuel
authors.initials: M
authors.lastname: Middendorf
authors.forename: Etay
authors.initials: E
authors.lastname: Ziv
authors.forename: Chris H
authors.initials: CH
authors.lastname: Wiggins
mesh_terms: D000465:Algorithms; D000818:Animals; D029721:Drosophila Proteins / Q000378:metabolism*; D004331:Drosophila melanogaster; D011485:Protein Binding
chemical_list: D029721:Drosophila Proteins
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