Inferring Influence Networks from Longitudinal Bipartite Relational Data

• Activity Number: 49 - Recent Advances in Statistical Inference on Graphs
• Type: Topic-Contributed
• Date/Time: Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Frank Marrs* and Bailey K Fosdick and Benjamin W Campbell and Skyler J Cranmer and Tobias Böhmelt
• Companies: Los Alamos National Laboratory and Colorado State University and Cover My Meds and The Ohio State University and University of Essex
• Keywords: temporal networks; weighted networks; tensor regression

Abstract

Longitudinal bipartite relational data characterize the evolution of relations between pairs of actors, where actors are of two distinct types and relations exist only between disparate actor types. A common goal is to understand which actor relations incite later actor relations. There are two primary existing approaches to this problem. The first projects the bipartite data in each time period to a unipartite network and uses existing unipartite network models. Unfortunately, information is lost in calculating the projection and generative models for networks obtained through this process are scarce. The second approach represents dependencies using two unipartite influence networks, corresponding to the two actor types. Existing models taking this approach are bilinear in the influence networks, creating challenges in computation and interpretation. We propose a novel generative model that permits estimation of weighted, directed influence networks and does not suffer from these shortcomings. The proposed model is linear in the influence networks, permitting inference using off-the-shelf software tools.