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Learning opinions in social networks

Publication ,  Conference
Conitzer, V; Panigrahi, D; Zhang, H
Published in: 37th International Conference on Machine Learning, ICML 2020
January 1, 2020

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 Scholars

Published In

37th International Conference on Machine Learning, ICML 2020

ISBN

9781713821120

Publication Date

January 1, 2020

Volume

PartF168147-3

Start / End Page

2100 / 2110
 

Citation

APA
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ICMJE
MLA
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Conitzer, V., Panigrahi, D., & Zhang, H. (2020). Learning opinions in social networks. In 37th International Conference on Machine Learning, ICML 2020 (Vol. PartF168147-3, pp. 2100–2110).
Conitzer, V., D. Panigrahi, and H. Zhang. “Learning opinions in social networks.” In 37th International Conference on Machine Learning, ICML 2020, PartF168147-3:2100–2110, 2020.
Conitzer V, Panigrahi D, Zhang H. Learning opinions in social networks. In: 37th International Conference on Machine Learning, ICML 2020. 2020. p. 2100–10.
Conitzer, V., et al. “Learning opinions in social networks.” 37th International Conference on Machine Learning, ICML 2020, vol. PartF168147-3, 2020, pp. 2100–10.
Conitzer V, Panigrahi D, Zhang H. Learning opinions in social networks. 37th International Conference on Machine Learning, ICML 2020. 2020. p. 2100–2110.

Published In

37th International Conference on Machine Learning, ICML 2020

ISBN

9781713821120

Publication Date

January 1, 2020

Volume

PartF168147-3

Start / End Page

2100 / 2110