Learning Opinions in Social Networks

@InProceedings{pmlr-v119-conitzer20a,
  title = 	 {Learning Opinions in Social Networks},
  author =       {Conitzer, Vincent and Panigrahi, Debmalya and Zhang, Hanrui},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {2122--2132},
  year = 	 {2020},
  editor = 	 {III, Hal Daumé and Singh, Aarti},
  volume = 	 {119},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--18 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v119/conitzer20a/conitzer20a.pdf},
  url = 	 {https://proceedings.mlr.press/v119/conitzer20a.html},
  abstract = 	 {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.}
}