  We propose a new way to measure inequalities such as the glass ceiling effect
in attributed networks. Existing measures typically rely solely on node degree
distribution or degree assortativity, but our approach goes beyond these
measures by using mutual information (based on Shannon and more generally,
Renyi entropy) between the conditional probability distributions of node
attributes given node degrees of adjacent nodes. We show that this mutual
information measure aligns with both the analytical structural inequality model
and historical publication data, making it a reliable approach to capture the
complexities of attributed networks. Specifically, we demonstrate this through
an analysis of citation networks. Moreover, we propose a stochastic
optimization algorithm using a parameterized conditional logit model for edge
addition, which outperforms a baseline uniform distribution. By recommending
links at random using this algorithm, we can mitigate the glass ceiling effect,
which is a crucial tool in addressing structural inequalities in networks.
