Guimerà Roger, Sales-Pardo Marta, Amaral Luís A Nunes
Northwestern Institute on Complex Systems (NICO) and Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Sep;76(3 Pt 2):036102. doi: 10.1103/PhysRevE.76.036102. Epub 2007 Sep 6.
Modularity is one of the most prominent properties of real-world complex networks. Here, we address the issue of module identification in two important classes of networks: bipartite networks and directed unipartite networks. Nodes in bipartite networks are divided into two nonoverlapping sets, and the links must have one end node from each set. Directed unipartite networks only have one type of node, but links have an origin and an end. We show that directed unipartite networks can be conveniently represented as bipartite networks for module identification purposes. We report on an approach especially suited for module detection in bipartite networks, and we define a set of random networks that enable us to validate the approach.
模块化是现实世界复杂网络最显著的特性之一。在此,我们探讨两类重要网络中的模块识别问题:二分网络和有向单分网络。二分网络中的节点被划分为两个不重叠的集合,且链接必须一端来自每个集合。有向单分网络仅有一种类型的节点,但链接有起点和终点。我们表明,出于模块识别目的,有向单分网络可以方便地表示为二分网络。我们报告了一种特别适用于二分网络中模块检测的方法,并定义了一组随机网络以验证该方法。