Network Analysis in Social Science

네트워크 분석 방법론


Social scientists are increasingly turning to network analysis as a way to gain insights into the structure and dynamics of social systems. Network analysis allows researchers to examine how social actors are connected to one another, how information and resources flow through social networks, and how social networks shape individual behavior and collective outcomes. In this course, students will learn the fundamental concepts and techniques of network analysis and explore how they can be applied to social science research using the R programming language.

Hands-on practice with R will be an integral part of the course. Students will use the R programming language to manipulate, visualize, and analyze network data. R packages such as igraph, statnet, vizNetwork, and networkD3 will be used for network visualization, analysis, and modeling. Students will be provided with R code and examples to practice the concepts covered in class. Assignments and projects will require students to apply their knowledge of network analysis to real-world social science problems using R.

This course is designed for graduate students and researchers in social sciences who want to expand their research methods and explore new avenues for analysis using R. Prior experience with R is not required, but students should be comfortable with basic statistical analysis and have some familiarity with programming concepts. By the end of this course, students will have a solid understanding of network analysis in social science research and the ability to apply it using R. They will be able to use their new skills to explore social systems and gain insights into how social networks shape individual behavior and collective outcomes.


Week 1: Introduction to network analysis in social science

  • Overview of social network theory and concepts

  • Introduction to network data collection and analysis

  • Basic network measures and visualization techniques using R

Week 2: Network data collection and preparation in R

  • Survey and interview techniques for network data collection

  • Data cleaning and preparation for network analysis in R

  • Ethical considerations in network data collection

Week 3: Network visualization and exploration in R

  • Network visualization and layout techniques using R

  • Network exploration and analysis using R software tools

  • Interactive network visualization for exploration and presentation in R

Week 4: Measures of centrality and power in networks using R

  • Degree centrality, betweenness centrality, and closeness centrality in R

  • Eigenvalue centrality and PageRank algorithm in R

  • Hubs and authorities and other measures of power in R

Week 5: Network clustering and community detection in R

  • Clustering algorithms and techniques for detecting communities in R

  • Modularity optimization and other community detection measures in R

  • Visualization of network clusters and communities in R

Week 6: Network dynamics and change over time in R

  • Models for network growth and evolution in R

  • Network diffusion models and spread of influence in R

  • Longitudinal network analysis and visualization in R

Week 7: Multiplex networks and multilevel analysis in R

  • Multiplex networks and their analysis in R

  • Multilevel network analysis and its applications in R

  • Network-based models for social systems in R

Week 8: Network models for social contagion and influence in R

  • Diffusion models and the spread of information and behavior in R

  • Contagion models and epidemics in social networks in R

  • Models for social influence and persuasion in R

Week 9: Network models for social support and health in R

  • Social support and its measurement in network analysis in R

  • Social network analysis of health and illness in R

  • Network models for health interventions and prevention in R

Week 10: Political networks and power relations in R

  • Network analysis of power and influence in politics in R

  • Political alliances and coalitions in networks in R

  • Network models for predicting elections and voting behavior in R

Week 11: Economic networks and market dynamics in R

  • Network analysis of economic systems and markets in R

  • Social networks and their influence on economic outcomes in R

  • Models for network-based entrepreneurship and innovation in R

Week 12: Cultural networks and artistic production in R

  • Cultural networks and their analysis in R

  • Social networks in the creative industries in R

  • Network models for artistic collaboration and production in R

Week 13: Social network interventions and applications in R

  • Network-based interventions in social systems in R

  • Network approaches to community building and development in R

  • Network analysis and social policy in R

Week 14: Advanced network analysis and future directions in R

  • Advanced topics in network analysis in R

  • Future directions and trends in network analysis research in R

  • Student project presentations and feedback in R

Week 15: Final project and wrap-up

  • Students will work on a final project applying network analysis techniques to a social science research question using R.

  • The instructor will provide guidance and feedback to the students throughout the project.

  • Students will present their final project to the class and receive feedback from their peers.

  • The final class will be a wrap-up and future directions in network analysis research using R.