Bias

Simulating systematic bias in attributed social networks and its effects on rankings of minority nodes

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.

Inequality in Networks

In many network settings, nodes can be grouped based on heterogeneous characteristics. For example, individuals in social networks differ on certain characteristics such as gender. Group membership can also affect how nodes form connections and thus the network structure, for example through tie formation mechanisms such as homophily. We model the effect of group membership on the documentation and formation of network structure. This allows us to gain a deeper understanding of issues such as inequality and marginalisation through the lens of network analysis.