Before revision:  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 paper, we model how such systematic uncertainty on edges of an attributed network can impact network analysis, in particular the ranking of nodes. We discuss how erroneous edge observations can be driven by external node attributes and the relative edge positions in the network , thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks.
After revision:  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 the literature. To investigate the effects of such errors we introduce a framework for simulating systematic bias in attributed networks. Our framework enables us to model erroneous edge observations can be driven by external node attributes and the relative edge positions in the network , thereby opening a path towards a systematic study of the effects of edge-uncertainty for various network analysis tasks.