Abstract

Missing data are a prominent issue wherever data can be collected and is an especially notable problem when dealing with network data. As network data describe dependencies between nodes, missing data can lead to a multitude of issues ranging from inaccurate estimates to differing inferential claims. When considering the secretive aspect of covert networks - such as criminal or terrorist networks - validated and reliable data are very difficult to obtain and thus leads to great concerns with missing and unreliable data in the discipline.

This project aims to discuss the considerations and nuances for addressing missing data in covert networks. Despite missing data being discussed in the social network analysis literature, there is not enough detail or discussion to explicitly describe the patterns by which missing data are missing. We explore various empirical covert network biases and represent them using explicit models for mechanisms by which missing data are generated. We further extend models for missingness to models for the sampling of edges in a graph in order to represent empirical biases in the process by which covert networks are observed. Simulations of these models are applied to empirical covert network datasets to evaluate the effects of specifying explicit patterns of missingness. Ultimately, this project aims to bridge disconnects between statistical assumptions involved in addressing missingness in network analysis and the harsh empirical data collection conditions of covert networks by introducing statistically principled methods of generating plausible missingness mechanisms.