Advances in Graph Neural Networks
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 20 May 2026 | Viewed by 28
Special Issue Editor
Special Issue Information
Dear Colleagues,
Graph-structured data has become ubiquitous in various real-world domains, such as social networks, biological systems, knowledge graphs, and recommendation systems. Traditional machine learning methods struggle to effectively capture the complex topological dependencies and high-dimensional interactions inherent in such data. Graph Neural Networks (GNNs) have emerged, exemplifying manifestations of powerful paradigm for addressing these challenges, enabling deep representation learning directly on graphs. With ongoing developments in architecture design, scalability, interpretability, and robustness, GNNs are rapidly evolving and expanding their impact across disciplines. This Special Issue focuses on advancing the theoretical foundations, computational techniques, and practical applications of GNNs, with the goal of pushing the boundaries of graph-based intelligence.
We invite high-quality submissions that explore innovative methods, frameworks, and applications related to Graph Neural Networks. Topics of interest include, but are not limited to, novel GNN architectures, scalable and efficient GNN training, self-supervised and unsupervised GNN learning, explainability and fairness in graph models, and privacy-preserving GNNs. We also welcome papers applying GNNs in diverse domains such as genomics, cybersecurity, social computing, financial modeling, and recommender systems. This Special Issue encourages interdisciplinary contributions and aims to foster collaboration across machine learning, data mining, network science, and domain-specific communities.
Dr. Chaozhuo Li
Guest Editor
Manuscript Submission Information
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Keywords
- graph neural networks
- graph representation learning
- social network analysis
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