Seeding a Simple Contagion *

Abstract

I propose a method for selecting seeds to maximize contagion. First, fit a random graph model using a coarse categorization of individuals. Next, compute a seed multiplier for each category-this is the average number of new infections a seed generates. Finally, seed the category with the highest multiplier. Relative to existing methods, my approach requires far less granular data, and it consumes less computing power-the problem scales with the number of categories, not the number of individuals. I validate the methodology through simulations using real network data.

Keywords: Seeding, Networks

JEL Classification: D85