CSIRO ICT Centre, North Ryde.
IEEE/ACM Trans Comput Biol Bioinform. 2012 Jan-Feb;9(1):66-78. doi: 10.1109/TCBB.2010.80. Epub 2010 Aug 20.
We analyze assortative mixing patterns of biological networks which are typically directed. We develop a theoretical background for analyzing mixing patterns in directed networks before applying them to specific biological networks. Two new quantities are introduced, namely the in-assortativity and the out-assortativity, which are shown to be useful in quantifying assortative mixing in directed networks. We also introduce the local (node level) assortativity quantities for in- and out-assortativity. Local assortativity profiles are the distributions of these local quantities over node degrees and can be used to analyze both canonical and real-world directed biological networks. Many biological networks, which have been previously classified as disassortative, are shown to be assortative with respect to these new measures. Finally, we demonstrate the use of local assortativity profiles in analyzing the functionalities of particular nodes and groups of nodes in real-world biological networks.
我们分析了生物网络的关联混合模式,这些网络通常是有向的。在将它们应用于特定的生物网络之前,我们为分析有向网络中的混合模式建立了理论背景。引入了两个新的量,即内关联度和外关联度,它们被证明在量化有向网络中的关联混合方面很有用。我们还引入了内关联度和外关联度的局部(节点级)关联度。局部关联度分布是这些局部量在节点度上的分布,可以用于分析规范的和真实世界的有向生物网络。许多以前被归类为不关联的生物网络,根据这些新的度量标准,它们被证明是关联的。最后,我们展示了在分析真实世界生物网络中特定节点和节点组的功能时使用局部关联度分布的方法。