Analysis of Partially Observed Networks via Exponential-family Random Network Models
Methodology
2013-03-07 v1
Abstract
Exponential-family random network (ERN) models specify a joint representation of both the dyads of a network and nodal characteristics. This class of models allow the nodal characteristics to be modelled as stochastic processes, expanding the range and realism of exponential-family approaches to network modelling. In this paper we develop a theory of inference for ERN models when only part of the network is observed, as well as specific methodology for missing data, including non-ignorable mechanisms for network-based sampling designs and for latent class models. In particular, we consider data collected via contact tracing, of considerable importance to infectious disease epidemiology and public health.
Keywords
Cite
@article{arxiv.1303.1219,
title = {Analysis of Partially Observed Networks via Exponential-family Random Network Models},
author = {Ian E. Fellows and Mark S. Handcock},
journal= {arXiv preprint arXiv:1303.1219},
year = {2013}
}