Systematic Edge Uncertainty
In this paper, we introduce a model for systematic edge uncertainty in attributed networks. Our model enables us to distinguish between erroneous edge observations that are driven by external node attributes or the network structure itself, thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks.
Most network 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. We model how such systematic uncertainty on edges of an attributed network can impact network analysis.