D02-04AM: Applied Social Network Analysis

This PhD level course is open to all researchers aiming at bringing their research to the next level – having tangible impact with their research in organizations, society, and policy. The course is suited for participants with both qualitative and quantitative backgrounds, in any social science field, at stage of their career or research development.

Social scientists are more relevant than ever today. With increasing data availability, advancing technologies, and professionalized research methods, they are uniquely positioned to address complex social, economic, political, and cultural challenges. In times of rapid societal transformation and uncertainty, social scientists have a timely opportunity to shape a future that is inclusive, fair, innovative, sustainable, and prosperous.

Social Network Analysis (SNA) is a powerful, interdisciplinary research framework that maps and measures connectivity in our interconnected world. It identifies key players, the roles of groups and communities, and the flow of information, resources, and influence within networks. By uncovering relational mechanisms and testing network-building and disruption scenarios, SNA supports informed decision-making at personal, organizational, and policy levels.

This course explores the potential and limitations of SNA to drive meaningful change. Through readings and practical applications, you’ll rethink how the world works and grapple with imperfect data and tools. You’ll also learn to make research actionable and impactful.

The course integrates SNA with mixed methods to expand analytic depth and practical interpretation, emphasizing data pre-processing and wrangling. Designed for interdisciplinary participants, it combines individual and group work on specific research projects, whether using your data or provided datasets. It covers new perspectives, software, and techniques, fostering a comprehensive understanding of SNA.

Here is what to expect from this learning experience:

Pre-course

• Interdisciplinary readings (network science, social network analysis, political network analysis, organizational network analysis, policy network analysis)
• Software fundamentals (tutorials of R and Gephi)

Course

Day 1

Social Network Analysis & Network Data – Past, Present and Future

Day 2

Insights at the Network Level – Understanding Complex Ecosystems

Day 3

Insights at the Community Level – Understanding Groups and Group Dynamics

Day 4

Insights at the Individual Level – Understanding Key Actors, Their Potential and Limitations

Day 5

Causality in Networks – Intro to Inferential Network Analysis & Impact Assessment

Post-course

• Feeling overwhelmed, excited, and developing a passion for networks 😊  

Learning Outcomes

After this course you will be able to:

1. Identify research opportunities and gaps in interdisciplinary literature that can be addressed with a relational perspective 
2. Design network analytical and mixed-methods applied research   
3. Map complex ecosystems, identify communities and key actors, and measure network mechanisms for building trust, optimizing communications, improving diversity and inclusion, leveraging popularity, bridging capacity and influence, and enabling collaboration 
4. Analyze, visualize, and recommend data-driven, evidence-based decision making in diverse areas

Target audience

This PhD level course is open to all researchers aiming at bringing their research to the next level – having tangible impact with their research in organizations, society, and policy. The course is suited for participants with both qualitative and quantitative backgrounds, in any social science field, at stage of their career or research development. 

Admission Requirements

In this class, you can choose to work with one or both network analysis software we cover, R (coding) and Gephi (point-and-click). We will cover basic R, the package ‘igraph’ in depth (‘network’ and ‘sna’ as supplementary), and the packages ‘ergm’ and ‘RSiena’ briefly. Previous familiarity with R is recommended. If you do not have an interest to learn R, you will work in Gephi, an open source network analysis and visualization software. Gephi is more limited than R in data processing and statistics, but it can do a good job for an explorative, descriptive and visual project.

Indicative list of readings

BOOKS 

Coscia, M. (2021). The Atlas for the Aspiring Network Scientist. https://www.networkatlas.eu/ 

Barabási, A.L. (2016). Network Science. Cambridge University Press. http://networksciencebook.com/ 

Robins, G. (2015). Doing Social Network Research: Network-Based Research Design for Social Scientists. Sage Publications. 

McCulloh, I., Armstrong, H., Johnson, A. (2013). Social Network Analysis with Applications. Hoboken: Wiley. 

Jackson, M.O. (2008). Social and Economic Networks. Vol. 3. Princeton: Princeton University Press. 

Monge, P. R., Contractor, N. S., Contractor, P. S., Peter, R., & Noshir, S. (2003). Theories of Communication Networks. Oxford University Press, USA.

Knoke, D. (1994). Political Networks: The Structural Perspective. Vol. 4. Cambridge University Press. 

Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Vol. 8. Cambridge University Press. 

ARTICLES 

Borgatti, S.P., Mehra, A., Brass, D.J. & Labianca, G. (2009). “Network analysis in the social sciences.” Science, 323(5916): 892-895. 

Borgatti, S.P. & Everett, M.G. (1992). “Notions of position in social network analysis.” Sociological Methodology: 1-35. 

Borgatti, S.P. & Everett, M.G. (1997). “Network analysis of 2-mode data.” Social Networks 19(3): 243-269. 

Borzel, T., Heard-Laureote, K. (2009). “Networks in multi-level governance: Concepts and contributions.” Journal of Public Policy, 29(2): 135-52. 

Brands, R. A. (2013). Cognitive social structures in social network research: A review. Journal of Organizational Behavior, 34(S1): S82-S103. 

Cranmer, S.J. & Desmarais, B.A. (2016). “A critique of dyadic design.” International Studies Quarterly, 0: 1-8. 

de Vaan, M. & Wang, D. (2020). Micro-structural foundations of network inequality: Evidence from a field experiment in professional networking, Social Networks, Open Access. 

Fowler, J.H., Heaney, M.T., Nickerson, D.W., Padgett, J.F. & Sinclair, B. (2011). “Causality in political networks.” American Politics Research, 39(2): 437-480. 

Granovetter, M. (1973). “The strength of weak ties.” American Journal of Sociology, 78(6): 1360-1380. 

Hafner-Burton, E., Kahler, M., & Montgomery, A. (2009). Network Analysis for International Relations. International Organization, 63(3), 559-592. doi:10.1017/S0020818309090195 

Kadushin, C. (2005). “Who benefits from network analysis: ethics of social networks research” Social Networks, 27(2): 139-53. 

Kinne, B.J. (2013). “Network Dynamics and the Evolution of International Cooperation.” American Political Science Review, 107(04):766–785. 

La Due Lake, R. & Huckfeldt, R. (1998). “Social capital, social networks, and political participation.” Political Psychology, 19(3): 567-584. 

Lazer, D. (2011). “Networks in political science: Back to the future.” PS: Political Science & Politics, 44(1): 61-68. 

McClurg, S.D. & Young, J.K. (2011). “Political networks.” PS: Political Science & Politics, 44(1): 39-43. 

Padgett, J.F. & Ansell, C.K. (1993). “Robust Action and the Rise of the Medici, 1400-1434.” American Journal of Sociology, 98(6): 1259-1319. 

Smith, J. A., & Moody, J. (2013). Structural effects of network sampling coverage I: Nodes missing at random. Social Networks, 35(4): 652-668. 

Smith, J. A., Moody, J., & Morgan, J. H. (2017). Network sampling coverage II: The effect of non-random missing data on network measurement. Social Networks, 48: 78-99. 

Snijders, T.A.B. (2011). Statistical models for social networks. Annual Review of Sociology, 37: 131-153. 

Strogatz, S.H. (2001). “Exploring complex networks.” Nature, 410(6825): 268-276. 

Ward, M.D., Siverson, R.M. & Cao, X. (2007). Disputes, democracies, and dependencies: A reexamination of the Kantian peace. American Journal of Political Science, 51(3): 583-601. 

Ward, M. D., Stovel, K., & Sacks, A. (2011). Network analysis and political science. Annual Review of Political Science, 14, 245-264.

OTHER READINGS

Barabasi, A.L. (2002). Linked: The New Science of Networks. Cambridge, Mass.: Perseus Pub. 

Fowler, J. and Christakis, N. (2011). Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. Little, Brown Spark.

Barabasi, A.L. (2018). The Formula: The Universal Laws of Success. Little, Brown and Company

Cross, R. (2021). Beyond Collaboration Overload. How to Work Smarter, Get Ahead, and Restore Your Well-Being. Harvard Business Review Press.

This description is subject to change at the discretion of the Instructor

2 Credits

For completion of all work before and during the course, as outlined by the Instructor, and 90% participation and attendance of the course.

2 Additional Credits

Course specific extra assignments. These can include submitting assignments before the course, daily assignments, and/or a final assignment to be completed after the course as decided by the Instructor.