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Introduction to Applied Social Network Analysis

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Course Dates and Times

Monday 5 to Friday 9 March 2018
15 hours over 5 days

Dimitris Christopoulos

Modul University – Vienna

This course is an introduction to the theory and methodology of social network analysis (SNA).  Particular attention will be paid to the theoretical foundations of SNA and to methodological insights of interest to political scientists.  Each session will consist of a lecture to be followed by a seminar and a demonstration of a theoretical application.  In the seminars we will discuss papers with methodological interest framed around questions related to politics with a view of evaluating research designs where networks and relations are a key parameter of the research question.  In the Seminars we will also familiarise with some key software (UCINET, NETDRAW) via demonstrations but note that this is not a course designed to teach a software platform.  Those unfamiliar with SNA will get an overview of the methods and the theoretical foundations of the relational perspective.  Those with some familiarity should get the opportunity to structure their knowledge.  The instructor will also offer the option of individual appointments for those seeking tailored advice on specific methodological or research design questions.

Tasks for ECTS Credits

  • Participants attending the course: 2 credits (pass/fail grade) The workload for the calculation of ECTS credits is based on the assumption that students attend classes and carry out the necessary reading and/or other work prior to, and after, classes.
  • Participants attending the course and completing one task (see below): 3 credits (to be graded)
  • Participants attending the course, and completing two tasks (see below): 4 credits (to be graded)


  1. Take-home exam (due by 17.00 pm on the Monday following the course)
  2. Take-home paper (due two weeks after the end of the course by Friday 17.00 pm)


Instructor Bio

Dimitris Christopoulos has worked on diverse research projects, and has published work on political social networks, governance and entrepreneurship.

He has extensive experience in delivering postgraduate courses on network methodology.

Dimitris has taught more than 400 graduate students on social networks in Universities across Europe including Barcelona, Bristol, Edinburgh, Essex, EUI, Milan and Roskilde. He is currently researching political entrepreneurship, sustainability policies, social entrepreneurship and leadership networks.

He is the editor of the journal Connections, Director of the Centre for Business and Enterprise at the Edinburgh Business School, and Dean of Executive Education at MU Vienna.


Why use network analysis?

A formal analysis of social networks can be employed to understand political, economic and social organisations and individuals.  The networks examined can be internal or external to institutional context; they can be formal or informal.  Social Network Analysis (henceforth SNA) can be employed to:

  • examine the interactions between nodes (human actors or organisations);
  • examine agents (i.e. nodes) concurrently with structure (i.e. networks);
  • examine all actor/agents concurrently
  • measure the resource and information flows between nodes and
  • map the way nodes cluster or cohere.

SNA can provide measures of the structural constraints on actors dependent not only on their own relations but also on the relations of other actors.  Embedding actors within the set of their interactions allows for insights on the distribution of power and the impact of social and political action. 

All social interaction can be viewed in network terms.  This is because the network heuristic captures the essence of human interaction, which are the relations between actors.  Formal network analysis can assist in the examination of individuals within their institutional context and relational frame.  Indeed some sociologists consider all social interactions to be contingent on networks. 

The network perspective can be seen as a paradigm with its foundations on the mathematics of graph theory and social interaction theories.  It is also a methodology that can be applied across scientific disciplines and has a well developed set of descriptive statistics and a growing set of interpretative analytic tools.  Most widely used are measures of centrality, density, transitivity, reciprocity and brokerage.  Other measures examine the way groups cohere, fraction or cluster.  There are hundreds of algorithms available to examine network properties.

SNA has been employed along other methodological tools in the study of political institutions and actors.  It easily complements what have been traditionally termed qualitative or quantitative techniques and is conducive to method triangulation. 

Course Objectives

This module is aimed at postgraduate researchers and scholars in the social sciences.  It is intended to introduce the theory and methods of Social Network Analysis with particular focus on politics.  Participants are not expected to have familiarity with SNA or competence in statistical analysis beyond an elementary understanding of frequency distributions.  


Teaching for this course is designed to be interactive and participatory.  Key theoretical contributions are presented in lectures that will take about ½ the class time.  Debating the material presented is strongly encouraged.


Course participants are encouraged to interact in debates structured around daily readings and the open questions offered below.  The methodological choices and steps to data collection of the seminar reading in bold will be critically examined.  These are articles explaining the research design and methodological choices in collecting SNA dataset.  The research design choices in the alternate article in the list may also be debated.   


These are short step-by-step introductions to a number of key routines, where the way to import, view and analyze data are outlined.  This is not a software course.  The main software employed (UCINET & NETDRAW) have good help screens, online resources and support groups.  They are intuitive in their use and work in a Windows PC environment.  The use of Linux or AppleMac operating platforms is possible but not recommended.    

In this course we will:

  • introduce some key theoretical concepts underlying social network analysis,
  • introduce some key methodological tools for the analysis of networks in social science research,
  • demonstrate the steps in producing some key network descriptive statistics,
  • introduce research design choices and the operationalisation of SNA research as well as the collection of SNA data.

This course is intended to stimulate the intellectual curiosity of scholars and provide a theoretical and methodological guide for those wishing to dwell further in network analysis. 

Core Reading

Borgatti, S., Everett, M., Johnson, J. (2013) Analyzing Social Networks. London: Sage.

Christopoulos, D., Diani M., Knoke, D. (forthcoming) Political Networks, Multiplexity, Power. Cambridge UP.

Hanneman & Riddle Introduction to Social Networks. Online resource:

Hennig, M., Brandes, U., Pfeffer, J., Mergel, I. (2013) Studying Social Networks: A Guide to Empirical Research. Frankfurt: Campus Verlag.

Knoke D., Yang, S. (2008) Social Network Analysis, 2nd edition. London: Sage.

Scott, J. (2017) Social Network Analysis 4th edition. London: Sage.

Further Reading

Carrington, P., Scott J., & Wasserman S. (2005) Models and Methods in Social Network Analysis. Cambridge UP.

Diani, M. (2015) The Cement of Civil Society. Cambridge UP.

Hollstein, B. et al. (eds) (2017) Networked Governance. Springer.

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

Robins, G. (2015) Doing Social Network Research. Sage.

Valente, T. (2010) Social Network Analysis and Health: Models, Methods and Applications. Oxford UP.

Wasserman, s. and Faust, K. (1994) Social Network Analysis: Methods and Applications. Cambridge UP.

Bibliography on software

Borgatti, S. P., Everett, M. G. and Freeman, L. C. (2002) UCINET 6 for windows: software for social network analysis.  Harvard: Analytic Technologies. Extensive and very instructive help menu.

De Nooy, M. Mvrar, A. and Batagelj, V. (2011) Exploratory Social Network Analysis with Pajek 2nd edition.  Cambridge: Cambridge UP.

Huisman, M. and van Duijn, M.A.J. (2005) ‘Software for Social Network Analysis’ in Models and Methods in Social Network Analysis ed. by P. J. Carrington et. al. Cambridge UP.

Snijders, T.A.B., C. Steglich, M. Schweinberger, M. Huisman  (2010) SIENA manual version 3.2. Released April 2010.  This has now migrated to R. All versions available at:

Computer literacy and elementary statistical competence is assumed.

Participants are not expected to be familiar with SNA or be competent in matrix algebra.  An elementary understanding of statistical concepts such as frequency distributions will be helpful in following practical demonstrations. 

Participants that anticipate to conduct their own SNA analysis are strongly encouraged to diligently read the online primers and other study material associated to this course.


Day Topic Details
Note Lectures will take half the allocated time, with the remainder split between seminars and application demonstrations.
1 Lecture Topic: Defining networks & relations

Seminar questions:

1) What are the benefits of a formal analysis of policy networks?

2) Are networks defined by their boundaries?


Application demonstrations: UCINET Introduction.

2 Lecture Topic: Centrality, Brokerage, Strong & Weak Ties.

Seminar questions:

3) Is power associated to network centrality?

4) Are weak ties more important than strong ones?

5) Are brokerage positions always advantageous?


Application demonstrations: UCINET Descriptive Statistics.

3 Lecture Topic: Collecting Network Data.

Seminar questions:

6) Are influence networks over-simplifying political contest?

7) Can we capture conflict in networks?

8) Why do your friends have more friends than you?


Application demonstrations: NETDRAW Graphs & Visualisation.

4 Lecture Topic: Multivariate & Multi-mode Descriptive Statistics.

Seminar questions:

9) Is network data qualitative or quantitative?

10) Can the analysis of policy networks augment other methods of policy analysis?


Application demonstrations: CINET Inference.

5 Lecture Topic: SNA in Political Science

Seminar questions:

11) Is politics power or policy oriented?

12) Are networks the cause or the outcome of agent interaction?


Application demonstrations: UCINET Inference.

Day Readings

Lecture Readings:

Scott, ch1

Borgatti et al. ch 1 & 2

Knoke & Yang, ch1

Hanneman & Riddle, ch 1&2


Seminar Readings:

Christopoulos, 2008


Lecture Readings:

Borgatti et al. ch 9 & 10

Knoke & Yang, ch2

Hanneman & Riddle, ch10, 8, 9


Seminar Readings:

Pfeffer et al. (2014)

Brass & Krackhardt, 2012


Lecture Readings:

Borgatti et al. ch 4, 5, 7 & 15

Knoke & Yang, ch3

Hanneman & Riddle, ch 3, 4 & 6

Scott, ch3


Seminar Readings:

Walther & Christopoulos, (2014a)


Lecture Readings:

Knoke & Yang, ch4 pp.45-61

Borgatti et al. ch 13 & 6

Hanneman & Riddle, ch 18 (section 1&2)

Scott ch4


Seminar Readings:

Gessel & Tesdahl (2015)

Christopoulos & Quaglia, (2009)


Lecture Readings:

Knoke (2011)

Borgatti et al. ch 8

Christopoulos et al. ch 1


Seminar Readings:

Skvoretz (2015)

Stokman & Zeggelink, 1996, pp77-81

Hollstein et al (2017) ch 4


Seminar Readings

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.

Lecture Readings

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.

Bold = Essential readings

Italic = discuss methodology & research design

Software Requirements

Please note: This is not designed as a “how-to” software course. Demonstrations of key algorithms with stock data will be offered in elaborating theoretical concepts.

Demo software (Windows PC exclusively)

UCINET 6 – free for 60 days- good for general analysis- great help menus

Netdraw – freeware – good for visual analysis and representation (packaged with UCINET)

Please install to your own laptop if you want to conduct the take home exercises and emulate  the demonstration routines. 

Advanced software (not employed in demos)

Pajek is highly recommended for those with large datasets or those interested in efficient blockmodelling algorithms.

Visone and Gephi are well esteemed for graphic representation and analysis. MPNET is part of a suite of software based on a windows interface for the use of exponential random graph model algorithms.

If you are familiar with R and the CRAN suite of statistical software, I would recommend the programmes: network, sna, statnet, igraph, ergm.  Also sand a collection of programmes and routines associated to the textbook by Kolaczyk and Csardi, (2014).


Hardware Requirements

Students should bring their laptops and download all key software beforehand.

Informal “data & methodology surgery” time will be offered to those requiring advice on their specific research questions.  



Bibliography on SNA methods and applications

Barabási, Albert-László. (2002). Linked: The New Science of Networks. Cambridge, MA: Perseus.

Buchanan, Mark (2002) Small World: Uncovering Nature’s Hidden Networks.  London: Wiedenfeld and Nicolson.

Burt, Ronald S. (1992). Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press.

Burt, R. S. (1998) 'The Gender of Social Capital' in Rationality and Society' vol10, pp5-46.

Burt, Ronald S. (2005). Brokerage and Closure: An Introduction to Social Capital. New York: Oxford University Press.

Borgatti, S. (1997) ‘Structural Holes: Unpacking Burt’s Redundancy Measures’ Connections, vol 20.  pp35-38.

Borgatti, S. and Foster, P. (2003) ‘The new paradigm in organizational research: a review and typology’ in Journal of Management, vol.29/6, pp.991-1013.

Borgatti, S.P., Carley, K., and Krackhardt, D. (2006). Robustness of Centrality Measures under Conditions of Imperfect Data. Social Networks 28: 124–136.

Borzel, T., Heard-Laureote, K (2009) ‘Networks in multi-level governance: Concepts and contributions’ Journal of Public Policy, vol 29, 2 pp 135-52.

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.

Brandes, U. Erlebach T. (2005) Network Analysis Methodological Foundations.  Springer Verlag.

Carrington, P., Scott J., & Wasserman S. (2005) Models and Methods in Social Network Analysis. Cambridge UP.

Ceddia, G., Christopoulos, D., Hernandez, Y., Zepharovich, E. (2017) ‘2017) vich, network topologies and climate change adaptation: a preliminary analysis of flood risk management in Austria’ Environmental Science and Policy, 77: 140-146.

Christopoulos, D. (2006) ‘Relational Attributes of Political Entrepreneurs: A Network Perspective’ Journal of European Public Policy, vol. 13/5.

Christopoulos, D. (2008) ‘The Governance of Networks: Heuristic or Formal Analysis?’ in Political Studies vol 54/2.

Christopoulos, D. (2009) 'Peer Esteem Snowballing: A methodology for expert surveys' in Eurostat Conference for New Techniques and Technologies for Statistics, Brussels, Conference Proceedings. pp. 171-179.

Christopoulos, D. (2014) ‘Elite Networks & Economic Development’, European Urban and Regional Studies

Christopoulos. D. (2016) 'The impact of social networks on leadership behaviour' Methodological Innovations vol9, DOI:10.1177/2059799116630649

Christopoulos, D., Diani, M., Knoke, D. (forthcoming, 2017) Political Networks, Multiplexity, Power. Cambridge University Press. 

Christopoulos, D. & Ingold, K. (2015) ‘Exceptional or just well connected? Political entrepreneurs and brokers in policy making’ The European Political Science Review, 7(3), 475-498, 2015 DOI: 10.1017/S1755773914000277  

Christopoulos, D. & Vogl, S. (2015) ‘The Motivation of Social Entrepreneurs: The roles, agendas and relations of altruistic economic actors’ The Journal of Social Entrepreneurship, 6(1), 1-30,   DOI: 10.1080/19420676.2014.954254

Christopoulos, D. and Quaglia, L. (2009) ‘Influence and Brokerage: Network Constraints in EU Banking Regulation’ Journal of Public Policy, vol.29/2, pp: 179-200.

Coleman, J. S (1988) 'Social capital in the creation of human capital', American Journal of Sociology Vol. 94, pp. 95 – 121

Coleman, J. (1990) Foundations of Social Theory. Cambridge: Belknap Press.

Committee on Network Science for Future Army Applications (2006) Network Science.  National Research Council. Washington DC: National Academies Press. Available through:

Cross, R., Parker, A. (2003) The Hidden Power of Social Networks; Understanding how work really gets done in organizations.  Harvard Business School Press.

Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Social network analysis for ego-nets: Social network analysis for actor-centred networks. Sage.

Degene, A. Forse, M. (1999) Introducing Social Networks. Sage.

Della Porta, D., & Diani, M. (Eds.). (2015). The Oxford handbook of social movements. Oxford University Press.

Diani, M. (2015). The cement of civil society. Cambridge University Press.

Diani, Mario and Doug McAdam (eds.). (2003). Social Movements and Networks: Relational Approaches to Collective Action. Oxford: Oxford UP.

Domínguez, S., & Hollstein, B. (Eds.). (2014). Mixed methods social networks research: Design and applications (Vol. 36). Cambridge University Press.

Doreian, Patrick, Vladimir Batagelj, and Anuška Ferligoj. (2005). Generalized Blockmodeling. New York: Cambridge University Press.

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Emirbrayer, M. and Goodwin, J. (1994) "Network Analysis, Culture, and the Problem of Agency," American Journal of Sociology, vol. 99/6.

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