Non Parametric Statistics of Dynamic Networks with distinguishable nodes
  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. 
Daniel Fraiman, Nicolas Fraiman, Ricardo Fraiman
cond-mat.dis-nn cond-mat.stat-mech physics.data-an q-bio.QM stat.ME