Inferring network mechanisms: the Drosophila melanogaster protein interaction network.
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.
Middendorf
Manuel
M
Department of Physics, College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA.
Ziv
Etay
E
Wiggins
Chris H
CH
D000465:Algorithms; D000818:Animals; D029721:Drosophila Proteins / Q000378:metabolism*; D004331:Drosophila melanogaster; D011485:Protein Binding
D029721:Drosophila Proteins
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