Non Parametric Statistics of Dynamic Networks with distinguishable nodes
Disordered Systems and Neural Networks
2017-04-18 v5 Statistical Mechanics
Data Analysis, Statistics and Probability
Quantitative Methods
Methodology
Abstract
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.
Cite
@article{arxiv.1408.3584,
title = {Non Parametric Statistics of Dynamic Networks with distinguishable nodes},
author = {Daniel Fraiman and Nicolas Fraiman and Ricardo Fraiman},
journal= {arXiv preprint arXiv:1408.3584},
year = {2017}
}
Comments
24 pages, 6 figures. Title changed, Test (2017)