Learning Opinions in Social Networks
Publication
, Conference
Conitzer, V; Panigrahi, D; Zhang, H
Published in: Proceedings of Machine Learning Research
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 sampleefficient 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
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2020
Volume
119
Citation
APA
Chicago
ICMJE
MLA
NLM
Conitzer, V., Panigrahi, D., & Zhang, H. (2020). Learning Opinions in Social Networks. In Proceedings of Machine Learning Research (Vol. 119).
Conitzer, V., D. Panigrahi, and H. Zhang. “Learning Opinions in Social Networks.” In Proceedings of Machine Learning Research, Vol. 119, 2020.
Conitzer V, Panigrahi D, Zhang H. Learning Opinions in Social Networks. In: Proceedings of Machine Learning Research. 2020.
Conitzer, V., et al. “Learning Opinions in Social Networks.” Proceedings of Machine Learning Research, vol. 119, 2020.
Conitzer V, Panigrahi D, Zhang H. Learning Opinions in Social Networks. Proceedings of Machine Learning Research. 2020.
Published In
Proceedings of Machine Learning Research
EISSN
2640-3498
Publication Date
January 1, 2020
Volume
119