Inferring Influence Networks from Longitudinal Bipartite Relational Data

TL;DR

This paper introduces a new linear generative model for inferring influence networks from longitudinal bipartite relational data, overcoming limitations of existing projection and bilinear models, with proven consistency and practical effectiveness.

Contribution

A novel linear influence network model that enables easier inference and interpretation, with theoretical guarantees and demonstrated performance on real and simulated data.

Findings

Model is consistent under misspecification.

Performs well in simulation studies.

Effectively analyzes international state interactions.

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 types. A common goal is to understand the temporal dependencies, specifically which actor relations incite later actor relations. There are two existing approaches to this problem. The first approach 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 \emph{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. We prove our estimator is consistent under cases of model misspecification and nearly asymptotically equivalent to the bilinear estimator. We demonstrate the performance of the proposed model in simulation studies and an analysis of weekly international state interactions.