Deadline Date: 31 December 2026
Social networks have evolved into highly complex systems characterized by massive data and intricate structural relationships. The integration of data intelligence—particularly large language models, knowledge graphs, and deep learning—provides effective computational frameworks for analyzing these interconnected environments. By mapping complex semantic representations and network topologies, these technologies offer systematic methods for processing unstructured data and extracting relational knowledge. This enables more reliable modeling of user behaviors, information diffusion, and community structures. Given the dynamic and large-scale nature of modern networks, developing robust, scalable, and interpretable algorithms has become an essential focus for both theoretical research and practical application.
This Special Issue is organized to present recent methodological improvements, computational models, and applied studies concerning data intelligence in social networks. We welcome original research articles and systematic reviews that address current computational challenges, algorithmic design, and data-driven modeling within this domain. Our objective is to foster a grounded academic discussion, providing a platform for researchers and practitioners to exchange ideas on how advanced artificial intelligence techniques can be reliably implemented to analyze and understand complex network systems.
Topics of interest (including but not limited to):
1. Computational Modeling in Social Networks: Data-driven methodologies, complex network analysis, and structural evolution modeling.
2. Large Language Models and Generative AI: Algorithmic approaches for social data processing, semantic understanding, and content generation.
3. Knowledge Graphs and Relational Reasoning: Knowledge representation learning, graph neural networks, and structural reasoning in interconnected systems.
4. Behavioral Modeling and Multi-Agent Systems: Computational models for individual and collective behavior, swarm intelligence, and dynamic network simulation.
5. Information Diffusion and Network Dynamics: Mathematical and computational approaches for tracking social influence, public opinion evolution, and anomaly detection.
6. Intelligent Recommendation and Decision Support: Algorithmic design for social recommendation, personalized information services, and data-driven decision-making.
7. Trustworthy Computing in Social Systems: Privacy protection, data security, algorithmic fairness, and robust processing of noisy or deceptive information.
8. Interdisciplinary Applications: Applied data intelligence and network analysis in domains such as education, healthcare, smart cities, and public services.
Editorial board
