Learning Opinions in Social Networks
Vincent Conitzer · Debmalya Panigrahi · Hanrui Zhang
2020 Poster
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.
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