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Friday 26 July 13:00–15:00 and 15:30–18:00
Saturday 27 July 09:00–12:30 and 14:00–17:30
In social and political research, we often encounter situations where the assumption of independent, autonomous action does not hold, and we intuit that our subjects of analysis are somehow dependent of each other.
These systems of interaction can be represented as networks, where actors (nodes) can be individuals, groups, organisations, countries, events, and the interactions can be friendships, collaborations, alliances, trade exchanges, communications, etc.
Network analysis is the appropriate methodology for mapping and measuring relationships among interdependent actors, uncovering principles of interaction and their implications for behaviour, status, and opportunities for action.
This course provides a hands-on overview of exploratory network analysis techniques and existing theoretical underpinnings in political and social sciences. By the end of it, you will be able to independently conduct basic exploratory analyses using different types of relational data, and make informed choices about further steps for inferential network analysis and confirmatory analyses.
The course combines workshop-style activities, using attendees’ own data and example datasets in two software environments, with discussions of network, political, and social theory in conducting exploratory network research.
Orsolya Vasarhelyi is an assistant professor at the Center for Collective Learning, and at the Institute of Data Analytics and Information Systems at Corvinus University in Budapest, Hungary.
Her research focuses on the gender differences in career development in project-based environments.
She is a Python enthusiast!
Network analysis has a long tradition in the social sciences and has made considerable contributions to our understanding of the world around us, from organisation to voting behaviour, and from leveraging power to building trust. With the rapid growth and development of network science, and the increasing availability of data, students can now formalise and explore considerably more networked phenomena.
Exploratory network analysis quantifies these social structures from multiple perspectives, allowing users to leverage and corroborate information about their phenomenon of interest from different levels of analysis – whole networks, communities, and local actions; organisational/event level, and individual level; direct and/or indirect connections; different types of interactions, etc.
The course’s main goal is to give you practical skills and help you make informed choices in exploring and validating networks of different sizes and types. To this end, we will cover:
We’ll address the debate of theory- versus data-driven hypothesis formulations, the treachery of an interdisciplinary vocabulary, and the potential of practical applications of network analysis to socio-political problems.
Class 1: Network data, Friday 13:00–15:00
Network data is quite peculiar compared to typical data for statistical analyses. Its format, storage, and meaning are not always straightforward. Understanding our data and getting them in the right form for analysis is the most important and often the most time-consuming part of research. We’ll cover data collection methods, typical database formats and network types.
Class 2: Network structures, Friday 15:30–18:00
The structure of a network can tell us a lot about the underlying relational processes and mechanisms at work (e.g. trust, control, preferential attachment, closure). At macro level, we explore the different network structures displayed in our diverse empirical data. We discuss what the main network properties tell us about our subjects of analysis and do our first network-level analyses (and hypothesis testing): degree distributions, centralisation.
During the second part of the class we try some transformation and visualisation techniques used in exploratory network analyses. We finish with a discussion on diversity of operationalisations and interpretations, using examples using examples from participants’ own work.
Class 3: Community detection & segregation, Saturday 09:00–12:30
Homophile is one of the key drivers of network formation. In this class, we take a closer look at clustering patterns and community detection, and quantifying segregation on networks. After the theoretical introduction, we learn how to conclude community detection and analyse segregation in social networks with a point-and-click software (Gephi) and R.
Class 4: Micro-level analyses, Saturday 14:00–17:30
We conclude the course with micro-level analyses. The positions different entities occupy in the network entail constraints and opportunities for their behaviour (e.g. brokerage, importance, prestige, influence). We will discuss centrality measures and different theories of tie formation applied to participants’ research, and explore models for hypothesis testing at the individual level. Finally, we wrap everything up, highlighting assumptions, opportunities, challenges, and limitations of exploratory network analysis in political science.
The course will cover only basic concepts and analytical techniques.
If you bring your own data, by the end of the course you will have a first exploratory analysis of your network, as well as a few theoretical leads related to their substantive applications.
Those who don’t come with data can still conduct a comprehensive exploratory network analysis, and gain inspiration for their next research project / thesis / article.
Please complete the mandatory readings beforehand. The bibliography is meant to help you explore the topics further, finding inspiration and the right tools for analysis, and getting to know some applications for this methodology within the scope of social sciences.
No previous knowledge of network analysis required. This course is suitable for anyone doing qualitative, quantitative, or mixed-methods research, at any stage of their research process.
| Day | Topic | Details | |
|---|---|---|---|
| 2.2 | Micro Level Analyses |
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| 1.1 | Network data |
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| 1.2 | Exploring network structures |
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| 2.1 | Community Detection & Segregation |
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Expect two short practical assignments. |
| Day | Readings |
|---|---|
| 1 |
Intro to network analysis in social and behavioural sciences
Network data
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| 2 |
Communities
Node Properties
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| 3 |
Wasserman and Faust (1994) – Part III (Structural and Locational Properties: pp. 167–215; 291–325; 461–467) Borgatti, Stephen P., and Martin G. Everett. ”Notions of position in social network analysis.”Sociological methodology(1992): 1-35 Journal articles Padgett, John F., and Christopher K. Ansell. (1993). ”Robust Action and the Rise of the Medici, 1400-1434.”American Journal of Sociology: 1259–1319 Burt, Ronald S. (2002). ”The social capital of structural holes.” In Meyer, Marshall. The New Economic Sociology: Developments in an Emerging Field. Russell Sage Foundation |
Please bring your own laptop.
I expect the group to be interdisciplinary, so I will cover two softwares: the point-and-click Gephi and the programming language R. Both are free and you should have them installed and working for the class.
R (RStudio) – preferably latest version (3.3.2), but earlier versions are fine.
Please bring your own laptop.
The following are extensions of different discussion threads we touch upon in class. They mostly cover basic and advanced topics in exploratory network analysis in social sciences – vocabulary, notation, methods, measures, validation, research design; and applications of network analysis to different socio-political problems – international relations, economics, voting behaviour, governance, social movements, etc.
Books
Barabási, Albert-László (2016) Network Science Cambridge University Press
Borgatti, Stephen P., Martin G. Everett, and Jeffrey C. Johnson. (2013). Analyzing Social Networks. SAGE Publications Limited.
Burt, Ronald S. (2002). ”The social capital of structural holes.” In Meyer, Marshall. The New Economic Sociology: Developments in an Emerging Field. Russell Sage Foundation.
Carrington, Peter J., John Scott, and Stanley Wasserman, eds. (2005). Models and Methods in Social Network Analysis. Vol. 28. Cambridge University Press.
De Nooy, Wouter, Andrej Mrvar, and Vladimir Batagelj. Exploratory social network analysis with Pajek. Vol. 27. Cambridge University Press, 2011.
Diani, Mario and Doug McAdam (eds.). (2003). Social Movements and Networks: Relational Approaches to Collective Action. Oxford: Oxford University Press.
Hanneman, Robert A., and Mark Riddle. (2005). Introduction to Social Network Methods Riverside, CA: University of California, Riverside
Huisman, Mark, and Marijtje A.J. Van Duijn. (2005). ”Software for Social Network Analysis.” In Carrington, Peter J., John Scott, and Stanley Wasserman, eds. Models and Methods in Social Network Analysis. Vol. 28. Cambridge University Press.
Jackson, Matthew O. (2008). Social and Economic Networks. Vol. 3. Princeton: Princeton University Press.
Knoke, David. (1994). Political Networks: The Structural Perspective. Vol. 4. Cambridge University Press.
Knoke, David, and Song Yang. (2008). Social Network Analysis (Quantitative Applications in the Social Sciences). Los Angeles: Sage Publications.
Lusher, Dean, Johan Koskinen, and Garry Robins. (2012). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.
Maoz, Zeev. (2010). Networks of Nations: The Evolution, Structure, and Impact of International Networks, 1816–2001. Vol. 32. Cambridge University Press.
McCulloh I., Armstrong, H., Johnson, A. (2013) Social Network Analysis with Applications. Hoboken: Wiley.
Robins, Garry. (2015). Doing Social Network Research: Network-Based Research Design for Social Scientists. Sage Publications.
Wasserman, Stanley, and Katherine Faust. (1994). Social Network Analysis: Methods and Applications. Vol. 8. Cambridge University Press.
Articles
Borgatti, Stephen P., Ajay Mehra, Daniel J. Brass, and Giuseppe Labianca. (2009). ”Network analysis in the social sciences.” Science, 323(5916): 892-895.
Borgatti, Stephen P., and Martin G. Everett. (1992). ”Notions of position in social network analysis.” Sociological Methodology: 1-35.
Borgatti, Stephen P., and Martin G. Everett. (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.
Butts, Carter T. (2008). ”Social network analysis: A methodological introduction.” Asian Journal of Social Psychology, 11(1): 13-41.
Cranmer, Skyler J., and Bruce A. Desmarais. (2016). ”A critique of dyadic design.” International Studies Quarterly, 0: 1-8.
Cranmer, Skyler J., Bruce A. Desmarais, and Elizabeth J. Menninga. (2012). ”Complex dependencies in the alliance network.” Conflict Management and Peace Science, 29(3): 279-313.
Cranmer, Skyler J., Philip Leifeld, Scott D. McClurg, and Meredith Rolfe. (2016). ”Navigating the range of statistical tools for inferential network analysis.” American Journal of Political Science.
Fowler, James H., Michael T. Heaney, David W. Nickerson, John F. Padgett, and Betsy Sinclair. (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.
Ingold, Karin, and Philip Leifeld. (2014). ”Structural and institutional determinants of influence reputation: a comparison of collaborative and adversarial policy networks in decision making and implementation.” Journal of Public Administration Research and Theory: muu043.
Kadushin, C. (2005). “Who benefits from network analysis: ethics of social networks research” Social Networks, 27(2): 139-53.
La Due Lake, Ronald, and Robert Huckfeldt. (1998). ”Social capital, social networks, and political participation.” Political Psychology 19(3): 567-584.
Lazer, David. (2011). ”Networks in political science: Back to the future.” PS: Political Science & Politics, 44(1): 61-68.
McClurg, Scott D., and Joseph K. Young. (2011). ”Political networks.” PS: Political Science & Politics, 44(1): 39-43.
Padgett, John F., and Christopher K. Ansell. (1993). ”Robust Action and the Rise of the Medici, 1400-1434.” American Journal of Sociology, 98(6): 1259-1319.
Strogatz, Steven H. (2001). ”Exploring complex networks.” Nature, 410(6825): 268-276.
Ulibarri, Nicola, and Tyler A. Scott. ”Linking network structure to collaborative governance.” Journal of Public Administration Research and Theory: muw041.