论文标题
加权定向网络中的分类混合
Assortative Mixing in Weighted Directed Networks
论文作者
论文摘要
网络的分类性是基于相似性（通常过多的顶点程度）与他人结合的趋势。在本文中，我们考虑了定向和无方向性的加权网络中的分类性。为此，我们建议考虑过多的顶点强度，而不是过多的程度，并表明加权网络中的分类性可以分为两种机制，我们称为连接效应和放大效应。为了捕获这些效果，我们引入了广义的分类系数。这种新系数允许对加权网络中的分类性进行更详细的解释和评估。此外，我们提出了一种使用折刀，引导程序和重新布线技术来评估分类性统计学意义的程序。通过对几个加权现实世界网络的分类结构进行深入分析，我们提出的广义分类系数的有用性证明了这一点。
A network's assortativity is the tendency of vertices to bond with others based on similarities, usually excess vertex degree. In this paper we consider assortativity in weighted networks, both directed and undirected. To this end, we propose to consider excess vertex strength, rather than excess degree, and show, that assortativity in weighted networks can be broken down into two mechanisms, which we refer to as the connection effect and the amplification effect. To capture these effects we introduce a generalised assortativity coefficient. This new coefficient allows for a more detailed interpretation and assessment of assortativity in weighted networks. Furthermore, we propose a procedure to assess the statistical significance of assortativity using jackknife, bootstrap and rewiring techniques. The usefulness of our proposed generalised assortativity coefficient is demonstrated by an in-depth analysis of the assortativity structure of several weighted real-world networks.
