论文标题
相似性矩阵的平均值用于汇总多路复用网络
Similarity matrix average for aggregating multiplex networks
论文作者
论文摘要
我们介绍了基于平均相似性矩阵的方法，目的是将多重网络的层集成到单个单体网络中。采用多重网络来建模各种现实世界的框架，例如社会，经济和生物结构中的多类关系。更具体地说，当不同性质（层）的关系（层）之间出现一组元素（节点）之间时，使用多重网络。研究多路复用网络的一种可能的方法是在所有层中汇总单个网络（MonoPlex）中的不同层（MonoPlex）。为了获得这样的汇总网络，我们提出了一种理论方法以及其实际的实现，这源于相似性矩阵平均值的概念。该方法最终应用于统计期刊的多重相似性网络，在该网络中，三个考虑的层分别基于共同的作者和共同编辑者表达了期刊的相似性。
We introduce a methodology based on averaging similarity matrices with the aim of integrating the layers of a multiplex network into a single monoplex network. Multiplex networks are adopted for modelling a wide variety of real-world frameworks, such as multi-type relations in social, economic and biological structures. More specifically, multiplex networks are used when relations of different nature (layers) arise between a set of elements from a given population (nodes). A possible approach for investigating multiplex networks consists in aggregating the different layers in a single network (monoplex) which is a valid representation -- in some sense -- of all the layers. In order to obtain such an aggregated network, we propose a theoretical approach -- along with its practical implementation -- which stems on the concept of similarity matrix average. This methodology is finally applied to a multiplex similarity network of statistical journals, where the three considered layers express the similarity of the journals based on co-citations, common authors and common editors, respectively.
