Artificial Intelligence Meets Complex Networks: From Structure to Intelligence Across Domains

Dear Colleagues,

Networks are foundational structures in nearly every scientific discipline—from biology, neuroscience, and epidemiology to economics, engineering, and social systems. They offer a universal language to describe interdependencies, flows, and dynamic interactions across components in complex systems. In parallel, Artificial Intelligence (AI)—including machine learning, deep learning, graph neural networks (GNNs), and transformer-based models—has revolutionized our ability to model, interpret, and act on large-scale, high-dimensional data.

For this Special Issue, Artificial Intelligence Meets Complex Networks: From Structure to Intelligence Across Domains, we welcome contributions on the fertile intersection of network science and AI. Our goal is to showcase how AI can both analyze and enhance networked systems, while networks themselves provide structure, interpretability, and inductive bias to AI models.

We seek submissions that integrate AI techniques with the structure, dynamics, and function of networks in any scientific field. Contributions may include theoretical advances, novel algorithms, or applications to real-world networked data—including biological networks (e.g., gene or neural networks), infrastructure (e.g., power grids, transport), social and communication networks, epidemiological models, and more.

This Issue provides a platform for researchers working on the following:

• Using networks to improve AI systems (e.g., GNNs, hypergraph learning);
• Using AI to analyze, simulate, or control complex networks;
• Developing new mathematical or algorithmic frameworks that unify network theory with AI;
• Applying AI + networks to understand and influence complex systems in the real world.

Topics of interest include, but are not limited to, the following:

• Graph-based deep learning and graph neural networks (GNNs);
• Hypergraph and multilayer network models;
• AI-driven discovery of structure and dynamics in networks;
• Temporal and dynamic network analysis using AI;
• Explainable and interpretable AI through network representations;
• Generative models for graphs (e.g., GANs, VAEs, diffusion models);
• Network applications in medicine, neuroscience, economics, ecology, and infrastructure;
• Digital twins, agent-based modeling, and networked simulations;
• Retrieval-Augmented Generation (RAG) and LLMs for knowledge graphs;
• Integration of multimodal data (e.g., omics, text, images) into unified graph models;
• System resilience, emergence, and failure prediction via AI + networks;
• Neuromorphic computing and biologically inspired architectures.

We welcome original research papers, methodological advances, critical reviews, and visionary position papers that demonstrate how AI and network science can jointly address the complexity of modern systems.

By bringing together experts in AI, network theory, data science, and domain-specific sciences, this Special Issue aims to foster multidisciplinary insights and accelerate progress in network science through AI.

Prof. Dr. Alexey Zaikin
Guest Editor

Manuscript Submission Information

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• graph-based deep learning
• graph neural networks (GNNs)
• hypergraph and multilayer network models
• explainable and interpretable AI
• generative models
• digital twins
• retrieval-augmented generation
• large language model
• neuromorphic computing
• biologically inspired architectures