网络模体以集合的形式出现：随机化过程中的相关性
Network motifs come in sets: correlations in the randomization process
摘要：
模体（motifs）--子图在特定网络中出现的频率明显高于随机网络集合中的频率--的识别已经成为一种普遍存在的方法，用于揭示从各种领域抽取的网络中潜在的重要子单元。我们发现，用于从真实网络变化子图生成系综的最常见算法以高度相关的方式计数，因此一个子图作为基序的状态可能不独立于其他子图的状态。我们证明了这种效应的问题，3-和4-节点基序鉴定转录调控网络的E。coli和革兰氏阳性菌S.其中通过边缘交换算法产生随机化网络（Milo等人，科学298：824，2002）。我们证明了3-节点子图之间的相关性是很容易解释的，并提出了一个信息论工具，可以用来识别任何大小的子图之间的相关性。
英文摘要：
The identification of motifs--subgraphs that appear significantly more often in a particular network than in an ensemble of randomized networks--has become a ubiquitous method for uncovering potentially important subunits within networks drawn from a wide variety of fields. We find that the most common algorithms used to generate the ensemble from the real network change subgraph counts in a highly correlated manner, so that one subgraph's status as a motif may not be independent from the statuses of the other subgraphs. We demonstrate this effect for the problem of 3- and 4-node motif identification in the transcriptional regulatory networks of E. coli and S. cerevisiae in which randomized networks are generated via an edge-swapping algorithm (Milo et al., Science 298:824, 2002). We show that correlations among 3-node subgraphs are easily interpreted, and we present an information-theoretic tool that may be used to identify correlations among subgraphs of any size.
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