Entropy-Aware Graph Neural Networks: Theory, Methods, and Applications

Dear Colleagues,

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. Despite their empirical success, the theoretical understanding of GNNs, particularly from the perspective of information theory, remains limited. As GNN architectures become increasingly deep, dynamic, and heterogeneous, issues related to information loss, over-smoothing, and representation degradation call for a principled, entropy-based perspective.

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. Moreover, integrating information geometry and Riemannian representations with GNNs offers new theoretical and algorithmic insights into stability, optimization, and representation learning.

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 (1) provide information-theoretic analysis of GNN mechanisms and architectures, (2) propose new entropy- or information-driven GNN methods, or (3) explore applications of entropy-aware graph learning in scientific, industrial, and social domains.

Dr. Junran Wu
Prof. Dr. Friedhelm Schwenker
Dr. Nan Wang
Guest Editors

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