Research Article
Fuzzy Graph Neural Networks: A Comprehensive Review of Uncertainty-Aware Graph Learning
@ARTICLE{10.4108/eetcasa.9483, author={Ngoc Dan Tran and Thi Nhung Tong and Thi Kim Phung Nguyen and Thi Hong Tu Nguyen}, title={Fuzzy Graph Neural Networks: A Comprehensive Review of Uncertainty-Aware Graph Learning}, journal={EAI Endorsed Transactions on Contex-aware Systems and Applications}, volume={10}, number={1}, publisher={EAI}, journal_a={CASA}, year={2025}, month={7}, keywords={Fuzzy Graph Neural Networks, Graph Neural Networks, Graph Representation Learning, Explainable}, doi={10.4108/eetcasa.9483} }- Ngoc Dan Tran
Thi Nhung Tong
Thi Kim Phung Nguyen
Thi Hong Tu Nguyen
Year: 2025
Fuzzy Graph Neural Networks: A Comprehensive Review of Uncertainty-Aware Graph Learning
CASA
EAI
DOI: 10.4108/eetcasa.9483
Abstract
Graph Neural Networks (GNNs) have become powerful tools for learning from graph-structured data. However, traditional GNNs often fail to address uncertainty inherent in many real-world applications. Fuzzy Graph Neural Networks (FGNNs) integrate fuzzy logic into GNNs to provide a robust mechanism for managing uncertainty, imprecision, and vagueness. This paper presents a comprehensive review of FGNNs, examining their theoretical underpinnings, methodologies, applications, challenges, and potential research directions.
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