Graph Neural Networks and Transformers for Intelligent Data-Driven Systems
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 61
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue invites original research articles on Graph Neural Networks (GNNs) and Transformers for Intelligent Data-Driven Systems. We aim to showcase innovative research advancing theoretical foundations, methodologies, and applications of GNNs and Transformers in areas such as natural language processing, graph-based reasoning, predictive modeling, and other data-driven intelligent systems. The goal is to foster interdisciplinary collaboration, promote knowledge transfer, and highlight technological breakthroughs in these rapidly evolving fields.
We welcome high-quality, original submissions that have not been previously published or are under consideration elsewhere. Contributions should provide novel insights, rigorous methodologies, or impactful applications of GNNs and Transformers.
Dr. Xiaodi Huang
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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
- transformers
- intelligent data-driven systems
- graph representation learning
- attention mechanisms
- self-supervised learning
- graph convolutional networks
- knowledge representation
- knowledge discovery
- sequence modeling
- graph embeddings
- deep neural architectures
- scalable graph algorithms
- multimodal learning
- explainable ai
- temporal graph networks
- data-driven decision making
- adaptive learning systems
- real-time data analytics
- graph-based decision systems
- intelligent information processing
- contextual data modeling
- dynamic graph learning
- semantic data integration
- predictive intelligence
- autonomous data systems
- multi-source data fusion
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