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Title: An unsupervised Bayesian neural network for truth discovery in social networks
Authors: Yang, Jielong
Tay, Wee Peng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Yang, J. & Tay, W. P. (2021). An unsupervised Bayesian neural network for truth discovery in social networks. IEEE Transactions On Knowledge and Data Engineering.
Project: MOE2018-T2-2-019 
Journal: IEEE Transactions on Knowledge and Data Engineering 
Abstract: The problem of estimating event truths from conflicting agent opinions in a social network is investigated. An autoencoder learns the complex relationships between event truths, agent reliabilities and agent observations. A Bayesian network model is proposed to guide the learning process by modeling the relationship of the autoencoder's outputs with different variables. At the same time, it also models the social relationships between agents in the network. The proposed approach is unsupervised and is applicable when ground truth labels of events are unavailable. A variational inference method is used to jointly estimate the hidden variables in the Bayesian network and the parameters in the autoencoder. Experiments on three real datasets demonstrate that our proposed approach is competitive with, and in most cases better than, several state-of-the-art benchmark methods.
ISSN: 1041-4347
DOI: 10.1109/TKDE.2021.3054853
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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