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Monday 25 February – Friday 1 March, 14:00–17:30 (finishing slightly earlier on Friday)
15 hours over five days
This course provides a hands-on overview of applied social network analysis techniques and their 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 in different contexts: politics, economics, sociology, psychology.
We begin with the practical challenges and solutions in working with network data, and then introduce you to network structures, actors’ positions within networks, and the implications of these for different behaviours. Towards the end of the week, we will cover the basics of hypothesis testing and network dynamics.
The course combines workshop-style activities, using participants’ own data and example datasets in any of two preferred software environments (point-and-click or coding), and making use of data visualisations, including discussions of network, political, and social theory in conducting social network research.
The class is not heavy in mathematical formulas, but the basics of network science will be covered and explained through practical examples.
Tasks for ECTS Credits
2 credits (pass/fail grade) Attend at least 90% of course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, class.
3 credits (to be graded) As above, and complete two short practical assignments:
4 credits (to be graded) As above, and write up a research paper on the data you analyse (short intro, research question, short literature review, data and methods description, descriptive analysis and two hypotheses tests, visualisation of results, interpretation of results, discussion and conclusions).
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. With the rapid growth and development of network science, and with the increasing availability of data, students can now formalise and explore many more networked phenomena – from international relations to policy and political relations.
Social network analysis offers quantitative assessments of relational eco-systems from multiple perspectives, allowing users to leverage and corroborate information from different levels of analysis – whole network, communities, and local action; organisational/event level, and individual level; direct and/or indirect connections; different types of connections, etc.
The course’s main goal is to equip you with practical network analytical skills and help you make theory-informed choices in exploring and validating networks of different sizes and types. To this end, we will cover five practical areas of research: working with network data, learning from the topological properties of networks, exploring actors’ network positions, testing hypotheses and understanding network dynamics. Each day, you will be able to work with your own data or example data will be provided for you. 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 sociopolitical problems.
Practical and theoretical exercises will help you formulate appropriate research questions, choose or collect the best available data, and make informed choices about how to analyse them and interpret the results.
Day 1: Working with Network Data
Network data is quite peculiar as compared to typical data for statistical analyses. Their format, storage, and meaning are not always straightforward. Getting the data in the right form for analysis is the most important and often the most time-consuming part of the research. We’ll briefly cover data collection methods, typical database formats, and try some transformation and visualisation techniques used in exploratory analyses. We finish with a discussion on diversity of operationalisations and interpretations, using examples from your own work.
Day 2: Understanding Network Structures
The structure of a network can tell you a lot about the underlying relational processes and mechanisms at work. 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 subject of analysis and do our first network-level analyses: degree distributions, centralisation, clustering patterns, communities. You’ll be introduced to the theoretical and technical complexities that span from the results – understanding mechanisms at work in various types of networks. We finish with a discussion on choosing productive avenues for further research based on network statistics at the whole-network level.
Day 3: Understanding Actors’ Positions in Networks
The positions different actors display in the network entail constraints and opportunities for their behaviour. We will discuss centrality measures and different theories of relationship formation applied to your research, and explore models for hypothesis testing at the individual level. The central discussion for this day will be the idea of causality in social networks, trying to understand causal pathways to network positions of actors. We finish with a discussion on choosing appropriate avenues for further research based on network statistics at the individual level.
Day 4: Testing Network Hypotheses
After learning the basics of descriptive statistics in networks, we cover the main techniques for testing network hypotheses at different levels of analysis (macro-level and individual-level): traditional statistical tests, regression models for networks. We endwith a discussion on the science and art of choosing the right regression models for networks, assumptions, implications and interpretations of results.
Day 5: Exploring Network Dynamics
We often have the opportunity to collect and work with temporal data about networks. In this session, we bring them all together: how network structures, positions, mechanisms and processes behave over time. We wrap the course up with opportunities, challenges, and limitations of conducting disciplinary, interdisciplinary and trans-disciplinary research using the toolkit of social network analysis.
This course covers only basic concepts and analytical techniques. If you come with your 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 your substantive application. If you don’t come with data, you'll still be able to conduct a comprehensive exploratory network analysis, and get inspiration for your next research project/thesis/article. Please complete the mandatory readings before class.
The bibliography can take you further on your own after the course, helping you find inspiration and the right tools for analysis, and familiarising you with some state-of-the-art applications in the social sciences.
No prior knowledge required.
This course is for those who have heard about network analysis and think it might be a useful toolkit in their own research.
Exploratory network analysis is suitable for anyone doing qualitative, quantitative or mixed-methods research.
Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
Please check your course format before registering.
Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.
In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.
|Note||Lectures will take half the allocated time, with the remainder split between seminars and application demonstrations.|
|Day 1||Working with Network Data||
|Day 2||Understanding Network Structures||
|Day 3||Understanding Actors’ Positions in Networks||
|Day 4||Testing Network Hypotheses||
|Day 5||Exploring Network Dynamics||
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 the course page 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.
Borgatti et al. ch 8
Christopoulos et al. ch 1
Stokman & Zeggelink, 1996, pp77-81
Hollstein et al (2017) ch 4
Brass, D.J., Krackhardt, D.M. (2012) “Power, Politics and Social Networks in Organizations” in Politics in Organizations: Theory and Research Considerations ed. by G. R. Ferris & D.C. Treadway. New York: Routledge. [copy available at author’s personal web]Borgatti, S. et al (2012)
Christopoulos, D. (2008) “The Governance of Networks: Heuristic or Formal Analysis?” in Political Studies vol 54/2.
Christopoulos, D. and Ingold, K. (2015) “Exceptional or Just Well Connected? Political entrepreneurs and brokers in policy making”. European Political Science Review.
Gesell,S.B., Tesdahl,E.A. (2015) "The 'Madre Sana' Data Set" Connections 35/2: 62-65.
Hollstein, B. et al. (eds) (2017) Networked Governance. Springer.
Pfeffer, J. Hollstein, B. Skvoretz, J. (2014) „ The Sunbelt 2013 Data: Mapping the field of Social Network AnalysisSkvoretz, (2015)"The South Carolina Network Exchange Datasets" Connections 35/2: 58-61.
Stokman, F. N. and Zeggelink E. (1996) “Is politics power or policy oriented? A comparative analysis of dynamic access models in policy networks” Journal of Mathematical Sociology, vol. 21: 77-111.
Walther, O. and Christopoulos, D. (2014a) “Islamic Terrorism and the Malian Rebellion: A Network Analysis”, Terrorism and Political Violence, vol. 26.
Walther, O., & Christopoulos, D. (2014b). The 2012 Malian Conflict Network. Connections, 34(1-2), 52-53.
Borgatti, S., Everett, M., Johnson, J. (2013) Analyzing Social Networks. London: Sage.
Christopoulos, D., Diani M., Knoke, D. (forthcoming) Political Networks, Multiplexity, Power. Cambridge University Press.
Hanneman & Riddle Introduction to Social Networks. Online resource:
Knoke D., Yang, S. (2008) Social Network Analysis, 2nd edition. Sage.
Knoke D. (2011) “Political Networks” in The Sage Handbook of Social Network Analysis ed by J. Scott and P. Carrington.
Scott, J. (2017) Social Network Analysis 4th edition. London: Sage.
Please bring your own laptop.
Since the make-up of the group is expected to be interdisciplinary, I will cover two types of software: a point-and-click one (Gephi or ORA-Lite) and a programming language (R).
All software is free, and you are expected to have them installed and working before the course.
Depending on the size of your data and what you want to do with them, generally, the more powerful the computer, the better.
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 sociopolitical problems – international relations, economics, voting behaviour, governance, social movements, etc.
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.
Kinne, Brandon J. 2013. 'Network Dynamics and the Evolution of International Cooperation.' American Political Science Review, 107(04):766–785.
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.
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.
Butts, Carter T. (2008). Social network analysis with sna. Journal of Statistical Software, 24(6), 1-51.
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.
Goodreau, S. M., Kitts, J. A., & Morris, M. (2009). Birds of a feather, or friend of a friend? Using exponential random graph models to investigate adolescent social networks. Demography, 46(1), 103-125.
Granovetter. M. (1973). 'The strength of weak ties.' American Journal of Sociology, 78(6): 1360-1380.
Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., and Morris, M. (2008), 'statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data,' Journal of Statistical Software, 24, 12–25.
Hunter, D. R., Krivitsky, P. N., and Schweinberger, M. (2012), 'Computational Statistical Methods for Social Network Models,' Journal of Computational and Graphical Statistics, 21, 856–882.
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.
Ripley, R. M., Snijders, T. A., & Preciado, P. (2011). Manual for RSIENA. University of Oxford, Department of Statistics, Nuffield College, 1.
Snijders, T. A. (2011). Statistical models for social networks. Annual Review of Sociology, 37, 131-153.
Snijders, Tom A. B., Gerhard G. van de Bunt and Christian E. G. Steglich. 2010. 'Introduction to Stochastic Actor-Based Models for Network Dynamics.' Social Networks, 32(1):44–60.
Steglich, C., Snijders, T. A., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40(1), 329-393.
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.
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.
Introduction to Exploratory Anaylsis
Social Networks: Theoretically Informed Analysis with UCINET
Advanced Social Network Analysis and Visualisation with R
Inferential Network Analysis
Introduction to Discourse Network Analysis (DNA)