Middendorf Manuel, Ziv Etay, Wiggins Chris H
Department of Physics, College of Physicians and Surgeons, Columbia University, New York, NY 10027, USA.
Proc Natl Acad Sci U S A. 2005 Mar 1;102(9):3192-7. doi: 10.1073/pnas.0409515102. Epub 2005 Feb 22.
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.
自然形成的网络呈现出揭示潜在生长机制的定量特征。最近提出了许多网络机制来重现特定属性，如度分布或聚类系数。我们提出了一种方法，利用机器学习中的判别工具来推断最能准确捕捉给定网络拓扑结构的机制。黑腹果蝇蛋白质网络被可靠且稳健地（对于噪声和训练数据子采样）分类为复制-突变-互补网络，而非优先连接、小世界网络以及无互补的复制-突变机制。系统分类而非对特定属性的统计研究，提供了一种判别方法来理解复杂网络的设计。
