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Thursday 27 - Saturday 29 July
10:00-12:00 and 14:00-17:00
Please see Timetable for full details.
In social and political research, we often encounter situations where the assumption of independent, autonomous action does not hold, and we have the intuition 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, organizations, 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 behavior, 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 the course, participants 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.
Silvia Fierăscu holds a PhD in Comparative Politics and Network Science from Central European University.
Her research focuses primarily on quality of governance, political-business relations, and statistical analyses of network data.
Silvia is involved in various interdisciplinary projects, translating complex problems into real-time applications for organisational management, political communication, and better governance.
Network analysis has a long tradition in the social sciences and has made considerable contributions to our understanding of the world around us, from organizational to voting behavior, and from leveraging power to building trust. With the rapid growth and development of network science, and with the increasing availability of data, students can now formalize 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; organizational/event level, and individual level; direct and/or indirect connections; different types of interactions, etc.
The course’s main goal is to equip attendees with practical network analysis skills and help them make theory-informed choices in exploring and validating networks of different sizes and types. To this end, we will cover three practical areas of research: working with network data, learning from the overall network structures, and exploring the implications of occupying certain positions in these networks. 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.
Day 1: Network data
Network data is quite peculiar as compared to typical data for statistical analyses. Its format, storage, and meaning are not always straight-forward. Understanding our data and getting them in the right form for analysis is the most important and often the most time consuming part of the research. We’ll cover data collection methods, typical database formats, and try some transformation and visualization techniques used in exploratory analyses. We finish off the first day with a discussion on diversity of operationalizations and interpretations, using examples from participants’ own work.
Day 2: Network structures
The structure of a network can tell a lot about the underlying relational processes and mechanisms at work (e.g., trust, control, preferential attachment, closure). At the 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, centralization, clustering patterns, communities. Participants will be introduced to the theoretical and technical complexities that emerge from the results. We finish the second day with a discussion on choosing productive avenues for further research based on statistics at the whole-network level.
Day 3: Network positions
We conclude the course with micro-level analyses. The positions different entities occupy in the network entail constraints and opportunities for their behavior (e.g., brokerage, importance, prestige, influence). We will discuss centrality measures and different theories of tie formation applied to the participants’ research, and explore models for hypothesis testing at the individual level. We will use the last part of this day to wrap everything up, highlighting assumptions, opportunities, challenges, and limitations of exploratory network analysis in political science.
The course will only cover the basic concepts and analytical techniques. Should attendees come with their own data, by the end of the class they will have a first exploratory analysis of their network, as well as a few theoretical leads related to their substantive applications. Those who don’t come with data will still be able to conduct a comprehensive exploratory network analysis, as well as get some inspiration for their next research project/thesis/article. Participants are expected to do the mandatory readings before class. The recommended bibliography is meant to help those taking the course explore the topics further on their own, finding inspiration and the right tools for the analysis, and getting to know some applications for this methodology within the scope of social sciences.
No previous or specific knowledge of network analysis is required to attend. To clarify, exploratory network analysis is suitable for participants doing qualitative, quantitative, as well as mixed-methods research, at any stage of their research process.
| Day | Topic | Details |
|---|---|---|
| Thursday | Network data |
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| Friday | Exploring network structures |
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| Saturday | Exploring network positions |
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Note: This is an applied workshop. Please bring your own laptop and have the software installed on your machine. Make sure that they work before coming to class. Materials about software installation of ORA, Gephi and R, a brief on data formatting , and some R example codes will be available on Moodle before the class starts. If you already have a dataset of interest, bring it along. If not, you’ll get access to example networks. Expect two short practical assignments.
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| Day | Readings |
|---|---|
| Thursday |
Intro to network analysis in social and behavioral sciences:
Network data:
Barabasi (2016) – Ch. 2 (Graph Theory). |
| Friday |
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| Saturday |
Journal Articles:
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. |
Attendees are expected to bring their own laptop. Since the make-up of the group is expected to be interdisciplinary, I will cover two software: a point-and-click one (ORA-Lite) and a programming language (R). Both software are free and participants are expected to have them installed and working for the class.
ORA Casos – software ORA-lite. Free trial version up to networks of 2,000 nodes.
For participants who have data larger than 2,000 nodes, Gephi is recommended as a free alternative to ORA, although the functionality in Gephi is much more reduced.
R (RStudio) – preferably latest version (3.3.2), but earlier versions are fine as well.
The following recommendations are intended as 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 behavior, governance, social movements, etc.
Books
Barabási, Albert-László. (2016). Network Science. Cambridge University Press. Available online at: http://barabasi.com/networksciencebook/
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, published in digital form at http://faculty.ucr.edu/~hanneman/.
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.