Chessa Alessandro, Crimaldi Irene, Riccaboni Massimo, Trapin Luca
IMT Institute for Advanced Studies, Lucca, Italy.
PLoS One. 2014 Oct 10;9(10):e109507. doi: 10.1371/journal.pone.0109507. eCollection 2014.
In this work we are interested in identifying clusters of "positional equivalent" actors, i.e. actors who play a similar role in a system. In particular, we analyze weighted bipartite networks that describes the relationships between actors on one side and features or traits on the other, together with the intensity level to which actors show their features. We develop a methodological approach that takes into account the underlying multivariate dependence among groups of actors. The idea is that positions in a network could be defined on the basis of the similar intensity levels that the actors exhibit in expressing some features, instead of just considering relationships that actors hold with each others. Moreover, we propose a new clustering procedure that exploits the potentiality of copula functions, a mathematical instrument for the modelization of the stochastic dependence structure. Our clustering algorithm can be applied both to binary and real-valued matrices. We validate it with simulations and applications to real-world data.
在这项工作中，我们感兴趣的是识别“位置等效”参与者的集群，即那些在系统中扮演类似角色的参与者。具体而言，我们分析加权二分网络，该网络描述了一方参与者与另一方特征或特质之间的关系，以及参与者展现其特征的强度水平。我们开发了一种方法，该方法考虑了参与者群体之间潜在的多变量依赖性。其理念是，网络中的位置可以基于参与者在表达某些特征时展现出的相似强度水平来定义，而不仅仅是考虑参与者之间的关系。此外，我们提出了一种新的聚类程序，该程序利用了Copula函数的潜力，Copula函数是一种用于随机依赖结构建模的数学工具。我们的聚类算法可应用于二元矩阵和实值矩阵。我们通过模拟和对实际数据的应用来验证它。
