Inferring network mechanisms:: The Drosophila melanogaster protein interaction network
Inferring network mechanisms:: The Drosophila melanogaster protein interaction network
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DOI：
10.1073/pnas.0409515102
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发表时间：
2005-03-01
影响因子：
11.1
通讯作者：
Wiggins, CH
中科院分区：
文献类型：
--
作者：
Middendorf, M;Ziv, E;Wiggins, CH
自然发生的网络表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的属性，如度分布或聚类系数。我们提出了一种利用机器学习中的判别工具，最准确地推断捕获给定网络拓扑的机制的方法。果蝇蛋白质网络被自信而稳健地（对噪声和训练数据子采样）归类为优先依附的复制-突变-互补网络、小世界网络和没有互补的复制-突变机制。系统分类，而不是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。
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.