Jonathan Januar
Jonathan Januar is a social network researcher from the University of Melbourne with an interest in developing statistical models that acknowledge the social context by which social network data are collected from. He is especially interested in the difficult data collection environments covert networks are constructed from and aims to develop pragmatic statistical tools to address observational biases in covert network analysis.
Session
When collecting covert network data, researchers prioritise finding edges without confirming the absence of a tie. Despite the theoretical relevance of null ties, it is clear that the identification of ties is more practical for the discovery of covert networks. Therefore, we explore the process by which covert networks are discovered as an edge sampling process. We develop the literature on edge sampling processes using the line graph and auto-logistic actor attribute model to systematically formulate biased sampling processes to reflect realistic sampling biases. We define what might be termed person of interest (POI) bias in the model to reflect the dependence between sampled edges. We present a few examples using empirical and simulated population networks. We conclude that biased sampling processes can result in highly structured observed networks even when the population network lacks structure. We then briefly consider some potential applications of the model-based edge sampling process to adaptive sampling design methodology.