The Inflated Value of Network Data for Diffusion

Identifying the optimal set of individuals to first receive information (“seeds”) in a social network is a widely-studied question with applications in marketing new technologies, ideas, or economic development programs like micro-finance. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for some frequently studied diffusion processes and a small x, randomly seeding s plus x individuals can prompt a larger cascade than optimally targeting the best s individuals. We prove our results for large classes of random networks and also show that they hold in simulations over several real-world networks. This suggests that returns to collecting and analyzing network data to identify the optimal seeds may not be economically significant. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.

Joint work with Mohammad Akbarpour and Suraj Malladi