检测局部网络图案
Detecting local network motifs
摘要：
研究所谓的真实网络的拓扑结构，即从社会学或生物学数据中获得的网络，在过去十年中已经成为一个主要的兴趣领域。处理它的一种方法是认为网络是由称为基序的小功能单元构建的，可以通过寻找在整个网络中出现的数量惊人的小子图来找到。在这篇文章中，我们建议通过网络中的局部过度表示来定义图案，并开发一种统计方法来检测它们，而不依赖于模拟。然后，我们说明了我们的程序在模拟和真实数据上的性能，恢复已知的生物相关图案。此外，我们解释了我们的方法如何给出一些信息的顶点在一个主题中的各自的角色。
英文摘要：
Studying the topology of so-called real networks, that is networks obtained from sociological or biological data for instance, has become a major field of interest in the last decade. One way to deal with it is to consider that networks are built from small functional units called motifs, which can be found by looking for small subgraphs whose numbers of occurrences in the whole network are surprisingly high. In this article, we propose to define motifs through a local overrepresentation in the network and develop a statistic to detect them without relying on simulations. We then illustrate the performance of our procedure on simulated and real data, recovering already known biologically relevant motifs. Moreover, we explain how our method gives some information about the respective roles of the vertices in a motif.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
