New Advances in Graph Neural Networks (GNNs) and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 65
Special Issue Editor
Special Issue Information
Dear Colleagues,
We sincerely invite you to submit your latest research achievements on Graph neural networks (GNNS) and their various applications, especially those focusing on the topic of "New Advances and Application Practices". This special issue is titled "New Advances in Graph Neural Networks and Applications".
In recent years, graph neural networks, as a powerful tool, have shown great potential in handling complex relational data and have been widely applied in multiple fields such as social network analysis, recommendation systems, bioinformatics, transportation networks, and intelligent perception. GNN can effectively capture the hidden information in large-scale heterogeneous graphs by learning the representations of nodes, edges and substructures, promoting breakthroughs in many key tasks. However, to achieve its efficient deployment and large-scale application in practical scenarios, there are still many challenges.
The main difficulties include: high computing costs, the adaptability of the model in resource-constrained environments, the defense ability against attacks, and the performance stability in dynamic and heterogeneous graphs. For this reason, the hot research directions include algorithm optimization, model compression, distributed training and robustness improvement, etc.
This special issue aims to present the latest technological breakthroughs, innovative applications and future research directions in the field of Graph Neural Networks. We welcome original research papers, review articles and case analyses, aiming to promote the joint exploration of the future development of GNN by the academic and industrial sectors.
We look forward to your wonderful submission and jointly usher in a new era of graph neural networks with global peers!
Prof. Dr. Hua Duan
Guest Editor
Manuscript Submission Information
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Keywords
- graph neural network
- machine learning
- applied mathematics
- computer vision
- artificial intelligence
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