(2022). Just a Few Seeds More: The Inflated Value of Network Data for Diffusion. with Suraj Malladi and Amin Saberi. The Simons Institute for the Theory of Computing. https://old.simons.berkeley.edu/talks/tbd-487
MLA
Just a Few Seeds More: The Inflated Value of Network Data for Diffusion. with Suraj Malladi and Amin Saberi. The Simons Institute for the Theory of Computing, Oct. 27, 2022, https://old.simons.berkeley.edu/talks/tbd-487
BibTex
@misc{ scivideos_22846,
doi = {},
url = {https://old.simons.berkeley.edu/talks/tbd-487},
author = {},
keywords = {},
language = {en},
title = {Just a Few Seeds More: The Inflated Value of Network Data for Diffusion. with Suraj Malladi and Amin Saberi},
publisher = {The Simons Institute for the Theory of Computing},
year = {2022},
month = {oct},
note = {22846 see, \url{https://scivideos.org/simons-institute/22846}}
}
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.