Advances in Complex Networks: Graph Theory, AI, and Data Science
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 June 2026 | Viewed by 755
Special Issue Editors
Interests: social network analysis; graph neural networks; intelligent analysis of agricultural data; smart fishery informatization
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; bioinformatics; AI; big data science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the study of complex networks has become a central topic across mathematics, computer science, physics, and engineering. The rapid growth of data-driven technologies and artificial intelligence (AI) has created new opportunities for understanding, modeling, and optimizing complex systems—ranging from social and biological networks to communication, transport, and information systems.
This Special Issue aims to bring together cutting-edge research and innovative methodologies at the intersection of graph theory, AI, and data science. It welcomes both theoretical developments and practical applications that address the challenges of analyzing, modeling, and leveraging complex network structures in real-world contexts.
Topics of interest include, but are not limited to, the following:
- Advanced theories and methods in graph theory and network science;
- AI and machine learning techniques for network analysis and prediction;
- Network representation learning and graph neural networks (GNNs);
- Link prediction, community detection, and network reconstruction in complex systems;
- Dynamic and multilayer networks modeling;
- Network robustness, resilience, and control;
- Applications in social, biological, transportation, energy, and information networks;
- Data-driven approaches to complex network inference and optimization.
This Special Issue seeks to foster interdisciplinary dialogue between researchers and practitioners in mathematics, data science, computer science, and engineering, promoting the development of novel tools and theories for understanding the structure and dynamics of complex interconnected systems.
Dr. Huan Wang
Dr. Shichao Liu
Guest Editors
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 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
- complex networks
- graph theory
- artificial intelligence
- data science
- network analysis
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