Connectionists: Entropy-Aware Graph Neural, Networks: Theory, Methods, , and Applications - SI in MDPI Entropy

[Friedhelm Schwenker]

*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 at uni-ulm.de
www:https://www.uni-ulm.de/in/neuroinformatik/institut/hidden/f-schwenker/
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