Seeding a Simple Contagion
36 Pages Posted: 16 Feb 2022 Last revised: 2 Feb 2023
Date Written: February 2, 2023
Abstract
I propose a methodology for selecting seeds to maximize contagion. First, fit a random graph model based on a coarse categorization of individuals. Then, compute a seed multiplier for each category---this describes the average number of new infections each seed generates. Finally, seed the category with the highest multiplier. Relative to existing methods, my approach consumes 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 for a sample of individuals. I validate the methodology through simulations using real network data.
Keywords: Seeding, Networks
JEL Classification: D85
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