AI-Driven Complex Networks: Structure, Dynamics, and Intelligent Applications

In an increasingly interconnected world, understanding the behavior and structure of complex networks has become essential across disciplines. These networks—spanning social interactions, biological systems, urban and technological infrastructures, communication systems, and beyond—exhibit intricate patterns that challenge traditional analytical approaches. Recent advances in artificial intelligence offer powerful tools to address these challenges, providing new ways to explore, model, and manipulate complex networked systems.

Complex networks underpin processes across science, technology, and society. With the rise of artificial intelligence, new opportunities have emerged to analyze, predict, and optimize the structure and dynamics of these networks. AI-driven methods enable the detection of hidden patterns, the forecasting of network evolution, and the design of intelligent systems that adapt to changing conditions.

By integrating perspectives from computer science, data science, physics, applied mathematics, engineering, and applied disciplines, this Research Topic seeks to highlight how AI-driven approaches are advancing the study and application of complex networks. The Research Topic aims to illuminate the potential of intelligent methods to reveal new insights into network structure and dynamics, while enabling innovative solutions in science, technology, and society.

This Research Topic invites contributions at the intersection of AI and complex networks. Topics of interest include, but are not limited to:
- Machine learning for network topology inference and prediction
- AI-based modeling of multilayer and temporal networks, adaptive and intelligent network control
- Graph neural networks and deep learning for network dynamics
- Large language models for modeling of, and intervention in, networks.
- Social network studies of sandboxed AI agents.
- Combination of AI methodologies and network theory to address resilience, robustness, and vulnerability
- Applications in domains such as transportation engineering, communication, epidemiology, biology, medicine, finance, and energy, and more.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Complex Networks, Network Dynamics, Machine Learning, Graph Neural Networks

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.