Modeling networks with textual edges

Oregon Convention Center 

Edges in many real-world networks are associated with rich text information, such as email communications between accounts and interactions between social media users. To better account for the rich text information, we propose a new latent space network model that treats texts as embedded vectors. We establish a set of identifiability conditions for the proposed model and formulate a projected gradient descent algorithm for model estimation. We further investigate theoretical properties of the iterates from the proposed algorithm. The efficacy of our method is demonstrated through simulations and an analysis of the Enron email dataset.