Just a Few Seeds More: The Value of Network Data for Diffusion

Akbarpour, Mohammad; Malladi, Suraj; Saberi, Amin

doi:10.1257/aer.20180798

Abstract

Identifying the optimal set of individuals to first receive information (鈥榮eeds鈥�) in a social network to maximize expected diffusion is a widely-studied question in many settings. Several studies propose network-centrality-based heuristics to select seeds likely to increase diffusion. Here we show that, for the classic independent cascade model of diffusion, either seeding a few more individuals at random can prompt a larger diffusion than optimal seeding, or optimal seeding itself results in limited spread. These findings hold across a broad range of random networks and are supported by simulations on real-world networks.

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