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Uncertainty Aware Semi-Supervised Learning on Graph Data
Xujiang Zhao · Feng Chen · Shu Hu · Jin-Hee Cho

Wed Dec 09 07:20 PM -- 07:30 PM (PST) @ Orals & Spotlights: Graph/Meta Learning/Software

Thanks to graph neural networks (GNNs), semi-supervised node classification has shown the state-of-the-art performance in graph data. However, GNNs have not considered different types of uncertainties associated with class probabilities to minimize risk of increasing misclassification under uncertainty in real life. In this work, we propose a multi-source uncertainty framework using a GNN that reflects various types of predictive uncertainties in both deep learning and belief/evidence theory domains for node classification predictions. By collecting evidence from the given labels of training nodes, the Graph-based Kernel Dirichlet distribution Estimation (GKDE) method is designed for accurately predicting node-level Dirichlet distributions and detecting out-of-distribution (OOD) nodes. We validated the outperformance of our proposed model compared to the state-of-the-art counterparts in terms of misclassification detection and OOD detection based on six real network datasets. We found that dissonance-based detection yielded the best results on misclassification detection while vacuity-based detection was the best for OOD detection. To clarify the reasons behind the results, we provided the theoretical proof that explains the relationships between different types of uncertainties considered in this work.

Author Information

Xujiang Zhao (The University of Texas at Dallas)
Feng Chen (UT Dallas)
Shu Hu (University at Buffalo, State University of New York)
Jin-Hee Cho (Virginia Tech)

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