Editorial
Fuzzy Message Passing in Graph Neural Networks: A First Approach to Uncertainty in Node Embeddings
@ARTICLE{10.4108/eetcasa.8947, author={Minh Tuan Duong}, title={Fuzzy Message Passing in Graph Neural Networks: A First Approach to Uncertainty in Node Embeddings}, journal={EAI Endorsed Transactions on Contex-aware Systems and Applications}, volume={10}, number={1}, publisher={EAI}, journal_a={CASA}, year={2025}, month={7}, keywords={Graph Neural Networks, Message Passing, Uncertainty, Node Embeddings, Max-Min Aggregation, Fuzzy Logic}, doi={10.4108/eetcasa.8947} }- Minh Tuan Duong
Year: 2025
Fuzzy Message Passing in Graph Neural Networks: A First Approach to Uncertainty in Node Embeddings
CASA
EAI
DOI: 10.4108/eetcasa.8947
Abstract
Graph Neural Networks (GNNs) have emerged as a powerful tool for learning representations in graph structured data. However, traditional message-passing mechanisms often struggle with uncertainty and noise in node features and graph topology. In this paper, we propose Fuzzy Message Passing (FMP), a novel approach that integrates fuzzy max-min aggregation into GNNs to improve robustness against uncertainty. Our method enhances node embeddings by leveraging fuzzy logic principles, ensuring better stability and interpretability in complex graph tasks. Experimental results on benchmark datasets demonstrate that FMP outperforms conventional message-passing schemes, particularly in scenarios with noisy or incomplete data.
Copyright © 2025 Duong Minh Tuan et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
