Uncertainty-Based Opinion Inference on Network Data Using Graph Convolutional Neural Networks

Xujiang Zhao, Feng Chen, Jin-Hee Cho

Abstract: In recent years, belief models, such as subjective logic (SL) and collective subjective logic (CSL), have been developed to model an opinion consisting of belief, disbelief, and uncertainty. However, these belief models are designed based on either predefined operators (e.g., discounting and consensus operators) or distribution assumptions (e.g., Markov random fields or MRFs) that are incapable of capturing the heterogeneity of the uncertainty information in large-scale network data. In this paper, we propose a general framework to model and infer heterogeneous uncertainty information in network data based on the state-of-the-art graph convolutional neural networks (GCN). This work is the first that employs a GCN to model the heterogeneous probability density function (PDF) of node-level variables. And then we project this PDF function into a subspace of PDF functions defined based on node-level opinions via knowledge distillation, which provides an effective prediction of the unknown opinion of some nodes based on the observed opinions of the other nodes. Through the extensive simulation experiments, we show that our proposed approach performs better than SL and CSL in predicting unknown opinions when using two road traffic datasets for the validation of the tested algorithms.