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Dynamic network prediction

Published online by Cambridge University Press:  09 July 2020

Ravi Goyal Open the ORCID record for Ravi Goyal [Opens in a new window]  and
Victor De Gruttola
Ravi Goyal*
Affiliation:
Mathematica
Victor De Gruttola
Affiliation:
Department of Biostatistics, Harvard School of Public Health (e-mail: degrut@hsph.harvard.edu)
*
*Corresponding author. Email: ravi.j.goyal@gmail.com
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Abstract

We present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population. The framework includes a novel and flexible procedure to sample dynamic networks given a probability distribution on evolving network properties; it permits the use of a broad class of approaches to model trends, seasonal variability, uncertainty, and changes in population composition. Current methods do not account for the variability in the observed historical networks when predicting the network structure; the proposed method provides a principled approach to incorporate uncertainty in prediction. This advance aids in the designing of network-based interventions, as development of such interventions often requires prediction of the network structure in the presence and absence of the intervention. Two simulation studies are conducted to demonstrate the usefulness of generating predicted networks when designing network-based interventions. The framework is also illustrated by investigating results of potential interventions on bill passage rates using a dynamic network that represents the sponsor/co-sponsor relationships among senators derived from bills introduced in the U.S. Senate from 2003 to 2016.

Keywords

dynamic networkcongruence class modelpredictionco-sponsorship network

Information

Type
Research Article
Information
Network Science , Volume 8 , Issue 4 , December 2020 , pp. 574 - 595
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

Action Editor: Stanley Wasserman

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