Decoupling Homophily and Reciprocity with Latent Space Network Models

Abstract

Networks form useful representations of data arising in various physical and social domains. In this work, we consider dynamic networks such as communication networks in which links connecting pairs of nodes appear over continuous time. We adopt a point process-based approach, and study latent space models which embed the nodes into Euclidean space. We propose models to capture two different aspects of dynamic network data: (i) communication occurs at a higher rate between individuals with similar features (homophily), and (ii) individuals tend to reciprocate communications from other nodes, but in a manner that varies across individuals. Our framework marries ideas from point process models, including Poisson and Hawkes processes, with ideas from latent space models of static networks. We evaluate our models over a range of tasks on real-world datasets and show that a dual latent space model, which accounts for heterogeneity in both reciprocity and homophily, significantly improves performance for both static and dynamic link prediction.