arXiv:1011.1717v1 [stat.AP] 8 Nov 2010
The Annals of Applied Statistics
2010, Vol. 4, No. 2, 533–534
c ? Institute of Mathematical Statistics, 2010
INTRODUCTION TO PAPERS ON THE MODELING AND
ANALYSIS OF NETWORK DATA—II
By Stephen E. Fienberg
Carnegie Mellon University
This issue of The Annals of Applied Statistics (Volume 4, No. 2) con-
tains the second part of a Special Section on the topic of network modeling.
The first part consisted of seven papers and appeared with a general in-
troduction [Fienberg (2010)] in Volume 4, No. 1. In Part II we include a
diverse collection of eight additional papers with applications spanning bio-
logical, informational and social networks, using techniques such as kriging
and anomaly detection, and variational approximations, as well as the study
of latent structure in both static and dynamical networks:
• In A State-Space Mixed Membership Blockmodel for Dynamic Network To-
mography, Xing, Fu and Song combine earlier approaches involving mixed
membership stochastic blockmodels for static networks with state-space
models for trajectories and use the new dynamic modeling approach to
analyze the Sampson’s network of noviates in a monastery, the email com-
munication network between the Enron employees and a rewiring gene
interaction network of the life cycle of the fruit fly.
• In Maximum Likelihood Estimation for Social Network Dynamics, Sni-
jders, Koskinen and Schweinberger develop a likelihood-based approach to
network panel data with an underlying Markov continuous-time stochastic
actor-oriented process. They use the new methods to reanalyze a friend-
ship network between 32 freshman students in a given discipline at a Dutch
university, observed over six waves at three-week intervals beginning at
the start of the academic year.
• Xu, Dyer and Owen use a semi-supervised learning on network graphs in
which response variables observed at one node are used to estimate missing
values at other nodes, by exploiting an underlying correlation structure
among nearby nodes. The methods they employ in Empirical Stationary
Correlations for Semi-supervised Learning on Graphs are rooted in ideas
Received May 2010.
This is an electronic reprint of the original article published by the
Institute of Mathematical Statistics in The Annals of Applied Statistics,
2010, Vol. 4, No. 2, 533–534. This reprint differs from the original in pagination
and typographic detail.