Document Type
Conference Paper
Rights
This item is available under a Creative Commons License for non-commercial use only
Disciplines
Statistics
Abstract
In this paper we present an estimation algorithm for Bayesian multiplex exponential random graphs (BmERGMs) under missing net- work data. Social actors are often connected with more than one type of relation, thus forming a multiplex network. It is important to consider these multiplex structures simultaneously when analyzing a multiplex network. The importance of proper models of multiplex network structures is even more pronounced under the issue of missing network data. The proposed algorithm is able to estimate BmERGMs under missing data and can be used to obtain proper multiple imputations for multiplex network structures. It is an extension of Bayesian exponential random graphs (BERGMs) as implemented in the Bergm package in R. We demonstrate the algorithm on a well known example, with and without artificially simulated missing data.
DOI
https://doi.org/10.21427/PME8-MT48
Recommended Citation
Krause, R. W. and Caimo, A. (2019). Missing Data Augmentation for Bayesian Exponential Random Multi-Graph Models.International Workshop on Complex Networks, vol. 221, pg. 63–72. doi:10.21427/PME8-MT48
Publication Details
International Workshop on Complex Networks, 221, 63 – 72. Springer.