Analysis of Social Networks

Social network analysis reveals how connections between individuals and groups influence their actions. This course covers fundamental principles, statistical methodologies, and practical applications in the field.

Course Content

• Introduction to Networks and Graphs
  • Foundational concepts in network structures and graph theory, including basic representations and data structures.
• Describing Network Structure
  • Methods for summarizing network characteristics such as centrality measures, degree distribution, and network density.
• Network Modeling
  • Statistical models for network structure, focusing on capturing relationships and dependencies.
• Exponential Random Graph Models (ERGMs)
  • Application of ERGMs to identify network patterns and relational dependencies.
• Stochastic Actor-Oriented Models (SAOMs)
  • Modeling network evolution over time, with a focus on actors’ behaviors and changes in network structure.
• Relational Event Models (REMs)
  • Dynamic network analysis for understanding sequences of events and interactions between network actors.
• Machine Learning for Network Analysis
  • Application of machine learning techniques in network data analysis, focusing on predictive modeling and pattern discovery.
• Deep Learning for Networks
  • Exploration of deep learning methods to uncover complex structures in large-scale networks, with applications in social and biological data.

This course combines theoretical concepts with practical applications, equipping students with essential tools for analyzing real-world network data across various domains.