Inferring the Maximum Likelihood Hierarchy in Social Networks

Individuals in social networks are often organized under some hierarchy such as a command structure. In many cases, when this structure is unknown, there is a need to discover hierarchical organization. In this paper, we propose a novel, simple, and flexible method based on maximum likelihood to infer social hierarchy from weighted social networks. We empirically evaluate our method against both simulated and real-world datasets and show that our approach accurately recovers the underlying, latent hierarchy.