Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, USA.
Nat Commun. 2012 May 29;3:864. doi: 10.1038/ncomms1847.
Complex networks in natural, social and technological systems generically exhibit an abundance of rich information. Extracting meaningful structural features from data is one of the most challenging tasks in network theory. Many methods and concepts have been proposed to address this problem such as centrality statistics, motifs, community clusters and backbones, but such schemes typically rely on external and arbitrary parameters. It is unknown whether generic networks permit the classification of elements without external intervention. Here we show that link salience is a robust approach to classifying network elements based on a consensus estimate of all nodes. A wide range of empirical networks exhibit a natural, network-implicit classification of links into qualitatively distinct groups, and the salient skeletons have generic statistical properties. Salience also predicts essential features of contagion phenomena on networks, and points towards a better understanding of universal features in empirical networks that are masked by their complexity.
复杂网络在自然、社会和技术系统中普遍表现出丰富的信息。从数据中提取有意义的结构特征是网络理论中最具挑战性的任务之一。已经提出了许多方法和概念来解决这个问题，如中心性统计、模式、社区聚类和骨干，但这些方案通常依赖于外部和任意的参数。目前还不清楚通用网络是否允许在没有外部干预的情况下对元素进行分类。在这里，我们表明链接显著性是一种基于所有节点的共识估计来对网络元素进行分类的稳健方法。广泛的实证网络表现出一种自然的、网络隐含的链接分类，将链接分为性质不同的组，而显著的骨架具有通用的统计特性。显著性还可以预测网络上传染现象的基本特征，并有助于更好地理解被其复杂性掩盖的实证网络中的普遍特征。
