Learning Opinions in Social Networks
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2122-2132, 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.