Statistical methods for dynamic network analysis have advanced greatly in the past decade. This article extends current estimation methods for dynamic network logistic regression (DNR) models, a subfamily of the Temporal Exponential-family Random Graph Models, to network panel data which contain missing data in the edge and/or vertex sets. We begin by reviewing DNR inference in the complete data case. We then provide a missing data framework for DNR families akin to that of Little and Rubin (2002) or Gile and Handcock (2010a). We discuss several methods for dealing with missing data, including multiple imputation (MI). We consider the computational complexity of the MI methods in the DNR case and propose a scalable, design-based approach that exploits the simplifying assumptions of DNR. We dub this technique the “complete-case” method. Finally, we examine the performance of this method via a simulation study of induced missingness in two classic network data sets.
Bibliographical noteFunding Information:
This work was supported in part by ONR award N00014-08-1-1015, ARO awards W911NF-14-1-0577 (YIP) and W911NF-14-1-0552, NSF award IIS-1526 736, and NIH/NICHD award 1R01HD068395-01.
- Dynamic network models
- Dynamic network models with missing data
- Dynamic network regression
- Exponential random graph models
- Logistic regression
- Missing data
- Temporal exponential random graph models