可区分节点动态网络的非参数统计
Non Parametric Statistics of Dynamic Networks with distinguishable nodes
摘要：
随机图和随机网络的研究在过去的几十年里有了爆炸性的发展。与此同时，网络序列的统计分析技术发展较少。在本文中，我们专注于网络序列与一个固定数量的标记节点和研究一些统计问题的非参数框架。我们引入自然的概念，中心和深度函数的网络，随着时间的推移。我们开发了几种统计技术，包括测试，监督和无监督分类，以及网络空间中主成分集的一些概念。给出了一些例子和渐近结果，以及两个实际数据的例子。
英文摘要：
The study of random graphs and networks had an explosive development in the last couple of decades. Meanwhile, techniques for the statistical analysis of sequences of networks were less developed. In this paper we focus on networks sequences with a fixed number of labeled nodes and study some statistical problems in a nonparametric framework. We introduce natural notions of center and a depth function for networks that evolve in time. We develop several statistical techniques including testing, supervised and unsupervised classification, and some notions of principal component sets in the space of networks. Some examples and asymptotic results are given, as well as two real data examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
