Eric Kolaczyk (McGill University)

Title: Minority representation and fairness in network ranking, with an application to school contact diary data.

Abstract: Considerations of bias, fairness and representation are a prerequisite of responsible modern statistics, including statistical network analysis. Systematic bias can arise in network analysis when observation errors depend on node attributes, potentially distorting node rankings and group representation. In this talk, we are motivated by a high school contact network constructed from self-reported contact diaries, where nodes represent students, directed edges represent reported contacts, and groups are defined by sex. For networks with two known groups of nodes, we define minority representation as the proportion of nodes from the smaller group among the top-ranked nodes. We characterize its asymptotic behavior in degree-based rankings under a two-group stochastic block model. We model observation error through group-dependent missing-edge mechanisms and develop statistical methods to estimate the error rates and test for systematic bias. When correction is necessary, we apply a ranking correction procedure that targets the estimated group representation expected in the absence of observation error. Application to the contact diary data provides evidence that male students are more likely to omit contacts with female students than vice versa. These findings highlight the importance of accounting for group-dependent observation errors when assessing and correcting group representation in network rankings. Joint work with Hui Shen and Peter MacDonald.

Location: 201 Av. du Président-Kennedy, local PK-5115, Montréal