A0314
Title: Preferential latent space models for networks with textual edges
Authors: Emma Jingfei Zhang - Emory University (United States) [presenting]
Abstract: Many real-world networks contain rich textual information at the edges, such as email networks where an edge between two nodes is an email exchange. Other examples include co-author networks and social media networks. The useful textual information carried in the edges is often discarded in most network analyses, resulting in an incomplete view of the relationships between nodes. The aim is to propose representing the text document between each pair of nodes as a vector counting the appearances of keywords extracted from the corpus and introduce a new and flexible preferential latent space network model that can offer direct insights into how contents of the textual exchanges modulate the relationships between nodes. Identifiability conditions are established for the proposed model, and model estimation is tackled with a computationally efficient projected gradient descent algorithm. The non-asymptotic error bound is further derived from the estimator from each step of the algorithm. The efficacy of the proposed method is demonstrated through simulations and an analysis of the Enron email network.
