Learning opinions in social networks

Conference Paper

We study the problem of learning opinions in social networks. The learner observes the states of some sample nodes from a social network, and tries to infer the states of other nodes, based on the structure of the network. We show that sample-efficient learning is impossible when the network exhibits strong noise, and give a polynomial-time algorithm for the problem with nearly optimal sample complexity when the network is sufficiently stable.

Duke Authors

Cited Authors

  • Conitzer, V; Panigrahi, D; Zhang, H

Published Date

  • January 1, 2020

Published In

  • 37th International Conference on Machine Learning, Icml 2020

Volume / Issue

  • PartF168147-3 /

Start / End Page

  • 2100 - 2110

International Standard Book Number 13 (ISBN-13)

  • 9781713821120

Citation Source

  • Scopus