Seeding a Simple Contagion

33 Pages Posted: 16 Feb 2022 Last revised: 10 May 2022

See all articles by Evan Sadler

Evan Sadler

Columbia University, Graduate School of Arts and Sciences, Department of Economics

Date Written: February 11, 2022

Abstract

This paper introduces a methodology for selecting seeds to maximize contagion using a coarse categorization of individuals. Within a large and flexible class of random graph models, I show how to compute a seed multiplier for each category---the average number of new infections a seed generates---and I propose randomly seeding the category with the highest multiplier. Relative to existing methods for targeted seeding, my approach requires far less computing power---the problem scales with the number of categories, not the number of individuals---and far less data---all we need are estimates for the first two moments of the degree distribution within each category and aggregated relational data on connections between individuals in different categories. I validate the methodology through simulations using real network data.

Keywords: Seeding, Networks

JEL Classification: D85

Suggested Citation

Sadler, Evan, Seeding a Simple Contagion (February 11, 2022). Available at SSRN: https://ssrn.com/abstract=4032812 or http://dx.doi.org/10.2139/ssrn.4032812

Evan Sadler (Contact Author)

Columbia University, Graduate School of Arts and Sciences, Department of Economics ( email )

420 W. 118th Street
New York, NY 10027
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
120
Abstract Views
350
rank
312,953
PlumX Metrics