  Network analysis provides powerful tools to learn about a variety of social
systems. However, most analyses implicitly assume that the considered
relational data is error-free, reliable and accurately reflects the system to
be analysed. Especially if the network consists of multiple groups, this
assumption conflicts with a range of systematic 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 that are driven by external node attributes or errors arising from
the (hidden) network structure itself. We exemplify how systematic inaccuracies
distort conclusions drawn from network analyses on the network analysis task of
minority representations in degree-based rankings. By analysing synthetic and
real networks with varying homophily levels and group sizes, we find that
introducing systematic edge errors can result both in a strongly increased or
decreased ranking of the minority. The observed effect depends both on the type
of edge error considered and level of homophily in the system. We thus conclude
that the implications of systematic bias in edge data depend on an interplay
between network topology and type of systematic error. This emphasises the need
for an error model framework as developed here, which provides a first step
towards studying the effects of systematic edge-uncertainty for various network
analysis tasks.
