In this work, we propose a model to examine how network growth interventions impact the position of minority nodes in degree rankings over time. We find that even extreme quotas do not increase minority representation in rankings if the actors in the network do not adopt homophilic behaviour. Thus, interventions need to be coordinated in order to improve the visibility of minorities.
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.
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.