摘要翻译：
我们提出了一个生物学驱动的量，孪生度，来评估网络中节点之间的局部相似性。一对结点的孪生性是指两个结点具有相同邻居的连通的、标号为n的子图的个数。利用图动物算法对四种不同的蛋白质相互作用网络中的每对节点（子图大小n=4～n=12)进行孪生度估计。其中包括一个大肠杆菌引脚和三个酿酒酵母引脚--每个引脚都是用最先进的高通量方法获得的。几乎在所有情况下，节点对的平均孪生度都远远高于通过切换链路获得的空模型的预期。对于所有n,我们观察到a型孪生体（非连接对）与B型孪生体（连接对）的比率不同，从而将原核生物大肠杆菌与真核生物酿酒酵母区分开来。由于基因的复制，相互作用的相似性是预期的，而酿酒酵母全基因组复制的视差已经被报道为共同聚类成相同的复合物。事实上，我们发现，与随机选择的对相比，这些副蛋白质被过度地表示为双胞胎。这些结果表明，孪生性可以从目前可用的PIN数据中检测祖先关系。
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英文标题：
《Node similarity within subgraphs of protein interaction networks》
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作者：
Orion Penner, Vishal Sood, Gabe Musso, Kim Baskerville, Peter
  Grassberger, Maya Paczuski
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Molecular Networks        分子网络
分类描述：Gene regulation, signal transduction, proteomics, metabolomics, gene and enzymatic networks
基因调控、信号转导、蛋白质组学、代谢组学、基因和酶网络
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一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
  We propose a biologically motivated quantity, twinness, to evaluate local similarity between nodes in a network. The twinness of a pair of nodes is the number of connected, labeled subgraphs of size n in which the two nodes possess identical neighbours. The graph animal algorithm is used to estimate twinness for each pair of nodes (for subgraph sizes n=4 to n=12) in four different protein interaction networks (PINs). These include an Escherichia coli PIN and three Saccharomyces cerevisiae PINs -- each obtained using state-of-the-art high throughput methods. In almost all cases, the average twinness of node pairs is vastly higher than expected from a null model obtained by switching links. For all n, we observe a difference in the ratio of type A twins (which are unlinked pairs) to type B twins (which are linked pairs) distinguishing the prokaryote E. coli from the eukaryote S. cerevisiae. Interaction similarity is expected due to gene duplication, and whole genome duplication paralogues in S. cerevisiae have been reported to co-cluster into the same complexes. Indeed, we find that these paralogous proteins are over-represented as twins compared to pairs chosen at random. These results indicate that twinness can detect ancestral relationships from currently available PIN data.
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PDF链接：
https://arxiv.org/pdf/707.2076