Just a Few Seeds More: Value of Network Information for Diffusion

48 Pages Posted: 1 Nov 2017 Last revised: 2 Sep 2020

See all articles by Mohammad Akbarpour

Mohammad Akbarpour

Stanford University

Suraj Malladi

Stanford University, Graduate School of Business

Amin Saberi

Stanford University - Department of Management Science & Engineering

Date Written: August 20, 2020

Abstract

Identifying the optimal set of individuals to first receive information (‘seeds’) in a social network is a widely-studied question in many settings, such as diffusion of information, spread of microfinance programs, and adoption of new technologies. 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 the classic SIR model of diffusion and some of its generalizations, randomly seeding s + x individuals can prompt a larger diffusion than optimally targeting the best s individuals, for a small x. We prove our results for large classes of random networks, and verify them in several small, real-world networks. Our results identify practically relevant settings under which collecting and analyzing network data to boost diffusion is not cost-effective.

Keywords: Diffusion, social network, seeding, word-of-mouth, influence

JEL Classification: D85, D83, O12, Z13

Suggested Citation

Akbarpour, Mohammad and Malladi, Suraj and Saberi, Amin, Just a Few Seeds More: Value of Network Information for Diffusion (August 20, 2020). Available at SSRN: https://ssrn.com/abstract=3062830 or http://dx.doi.org/10.2139/ssrn.3062830

Mohammad Akbarpour (Contact Author)

Stanford University ( email )

Suraj Malladi

Stanford University, Graduate School of Business ( email )

Stanford, CA
United States

Amin Saberi

Stanford University - Department of Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

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