Inferring Influence Networks from Longitudinal Bipartite Relational Data
| Autor: | Bailey K. Fosdick, Skyler J. Cranmer, Tobias Böhmelt, Frank W. Marrs, Benjamin W. Campbell |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Theoretical computer science Computer science Relational database 05 social sciences 01 natural sciences Methodology (stat.ME) 010104 statistics & probability 0502 economics and business Bipartite graph Discrete Mathematics and Combinatorics 0101 mathematics Statistics Probability and Uncertainty Statistics - Methodology 050205 econometrics |
| DOI: | 10.6084/m9.figshare.10564073 |
| Popis: | 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 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. 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. Supplementary materials for this article are available online. |
| Databáze: | OpenAIRE |
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