I. Machine Learning Theory and Methods
Supervised, unsupervised, and semi-supervised learning
Deep learning and architecture design
Reinforcement learning and meta-learning
Automated machine learning (AutoML)
Graph learning and graph neural networks
Federated learning and privacy protection
Model interpretability and fairness
Causal inference
Large-scale optimization algorithms
Green AI and sustainable computing

II. Social Computing Theory and Methods
Social network analysis and evolution
Swarm intelligence and multi-agent systems
User behavior and emotion computing
Social media dissemination and event detection
Social attitudes and cultural modeling
Online collaboration and crowdsourcing

III. Digital Society
Digital manufacturing
Digital economy
Digital management
Digital learning
Digital communication
Digital transportation
Digital community
Digital governance
Digital agriculture and water management
Digital healthcare
Digital infrastructure

IV. Cross-Integration Technologies and Systems
Integration of social networks and machine learning
Multi-modal data fusion
Adaptive social system algorithms
Intelligent recommendation and personalized services
Fake news detection and intervention
Social influence and dissemination prediction
Efficient social computing systems

V. Intelligent Transportation and Urban Management
Intelligent transportation systems
Autonomous driving and vehicular networks
Urban traffic congestion optimization
Intelligent public transportation scheduling
AI applications in urban planning
Urban risk management and intelligent early warning
Data-driven urban governance
Urban logistics and distribution optimization

VI. Application Practices
Smart cities and public services
Social analysis in healthcare
Intelligent educational learning systems
E-commerce user analysis and marketing
Financial risk control and fraud detection
Ethics, privacy, and fairness
Large-scale system deployment