Seeding a Simple Contagion

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