Improving Centrality Fairness in Algorithmic Link-Recommendations

Link recommendation algorithms are used in online social networks to recommend new connections (e.g., friends or followees) to users. These algorithms can reduce the visibility of certain demographic groups. Recent approaches aim at adapting embedding-based methods to create unbiased network representations which, in turn, can be used to recommend connections in a fairer way. It remains unclear how these methods affect the network centrality of groups in social networks. Here, we evaluate how recommendations based on Fairwalk (a well-known method to generate fair graph embeddings) impact groups' betweenness centrality. We find that Fairwalk only ensures fair betweenness centrality for a narrow combination of group homophily. We propose a new method (Adaptive-alpha) that ensures fair centrality of various sensitive groups while maintaining similar utility when evaluated on synthetic networks and an empirical social network.