Graph Neural Networks: Theory, Methods and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 January 2026 | Viewed by 20
Special Issue Editor
Special Issue Information
Dear Colleagues,
Graph neural networks (GNNs) have emerged as powerful frameworks that can be used to learn representations of graph-structured data, enabling breakthroughs in diverse domains such as social network analysis, bioinformatics, recommender systems, and traffic prediction. Despite their success, significant challenges remain in terms of their theoretical foundations, scalability, interpretability, and robustness.
This Special Issue invites authors to submit high-quality original research and review articles that advance the theory, methodologies, and applications of GNNs. This includes contributions that address fundamental challenges, propose novel architectures, and explore innovative applications across interdisciplinary fields.
We welcome submissions on (but not limited to) the following topics:
- Theoretical Advances:
- Expressiveness and representational limits of GNNs;
- Graph neural operators and spectral-based approaches;
- Theoretical analysis of GNN generalization and robustness;
- Equivariance, invariance, and symmetry in GNNs;
- Graph representation learning and embeddings.
- Methodological Innovations:
- Scalable and efficient GNN architectures;
- Self-supervised and unsupervised graph learning;
- Dynamic and temporal graph neural networks;
- Heterogeneous, multi-relational, and knowledge graph embeddings;
- Explainability and interpretability in GNNs;
- Adversarial robustness and defense strategies.
- Applications:
- GNNs for social networks, recommender systems, and fault diagnosis
- Bioinformatics, drug discovery, and healthcare applications;
- Computer vision, scene graphs, and geometric deep learning;
- Natural language processing and knowledge graphs;
- Financial modeling, traffic prediction, and IoT applications.
Dr. Ruonan Liu
Guest Editor
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- graph neural networks
- graph representation learning
- GNN architectures
- GNN applications
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