*Entropy-Aware Graph Neural,Networks: Theory, Methods,,and Applications* - Special Issue in MDPI Entropy For submission information, please visit the SI webpage: https://www.mdpi.com/journal/entropy/special_issues/1028YT5CB9 Submission deadline: June 30, 2026 Guest Editors: Junran Wu, Nan Wang, Friedhelm Schwenker Graph neural networks (GNNs) have emerged as a fundamental framework for learning from graph-structured data, demonstrating remarkable success across domains such as social network analysis, recommender systems, bioinformatics, and financial modeling. Entropy and information-theoretic principles provide a natural lens for analyzing the expressivity, generalization, and robustness of GNNs. From the viewpoint of Fisher information, mutual information, and information bottlenecks, entropy-aware frameworks can help explain and improve the propagation, compression, and preservation of structural information in networks. This Special Issue aims to advance the understanding of entropy-aware graph neural networks by bridging information theory and graph representation learning. We invite original research and review articles that - provide information-theoretic analysis of GNN mechanisms and architectures, - propose new entropy- or information-driven GNN methods, or - explore applications of entropy-aware graph learning in scientific, industrial, and social domains. -- Prof. Dr. Friedhelm Schwenker University of Ulm Institute of Neural Information Processing D-89069 Ulm, Germany phone: +49-731-50-24159 fax: +49-731-50-24156 email:friedhelm.schwenker@uni-ulm.de www:https://www.uni-ulm.de/in/neuroinformatik/institut/hidden/f-schwenker/