Systematic edge uncertainty
Network analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered data is error-free and reliable. Especially if the network consists of multiple groups, this assumption conflicts with the range of systematic reporting biases, measurement errors and other inaccuracies that are well documented in our community. In this project, we model how such systematic uncertainty on edges of an attributed network can impact network analysis, in particular the ranking of nodes. Our model enables researchers to include systematic edge-uncertainty in their analyses and thereby better account for the role of minorities in social networks.