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
Publication Date
January 1, 2020
Volume
PartF168147-3
Start / End Page
2100 / 2110
Citation
APA
Chicago
ICMJE
MLA
NLM
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
Publication Date
January 1, 2020
Volume
PartF168147-3
Start / End Page
2100 / 2110