agneessens

Filip Agneessens is a Senior Lecturer at the Surrey Business School (University of Surrey). He has published a number of papers on social network analysis in leading social science journals, including the journal Social Networks. Together with Martin Everett he was a guest-editor for a special issue on two-mode social network analysis in the journal Social Networks. He has previously taught a number of courses on network theory, social network methods and social network analysis applied to organisations, sociology and political sciences. His research centres on network formation within organisations and their impact on attitudes and behaviour of individual employees. He has also been working on the development of mathematical models for analysing networks, on imputation techniques for solving missing data problems and on methods for building social support typologies for ego-networks.

Course Content
This course will provide a practical, but comprehensive introduction to the analysis of social networks. Social network analysis takes the view that social research should not solely focus on the individual unit of analysis, but rather emphasises that researchers should also incorporate the social relations (networks) that connect these individual units (actors). For example, we might be interested in friendship among schoolchildren, trust among employees, collaboration among NGOs, exchanges of resources among companies, or conflict among nations.

The course focuses on the description and visualisation of social network data using UCINET. We will concentrate on uncovering structural properties of the network (e.g. density, homophily, and clustering), as well as on how to identify important persons in a network (e.g. degree centrality, structural holes, …). We will also pay attention to the detection of subgroups and deal with basic hypothesis testing for social network analysis. Throughout the course some classic theories that focus on network processes (e.g. related to homophily, centrality measures, structural holes, Granovetter’s strength of weak ties and small worlds) will be discussed.

Course Objectives
Participants will obtain a thorough understanding of the main theories and (basic) methods of social network analysis. Having taken this module, students should be able to design and carry out a social network research studies, as well as be able to interpret network analyses in a consultancy setting.

Course Prerequisites
Participants need to be familiar with basic mathematical notation provided in an elementary introductory statistics module (e.g. know when to reject a null hypothesis and be able to read a regression output). Emphasis is on understanding and interpretative methods, not on the underlying mathematics. Participants should also be comfortable learning new menu-driven software of complexity, such as Microsoft Excel.
Representative Background Reading
Scott, J. 2000. Social Network Analysis. Newbury Park CA, Sage.

Required Reading

Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage. (Please note that this book will be provided by the summer school on arrival).

This course will provide a practical, but comprehensive introduction to the analysis of social networks. Social network analysis takes the view that social research should not solely focus on the individual unit of analysis, but rather emphasises that researchers should also incorporate the social relations (networks) that connect these individual units (actors). For example, we might be interested in friendship among schoolchildren, trust among employees, collaboration among NGOs, exchanges of resources among companies, or conflict among nations.

The course focuses on the description and visualisation of social network data using UCINET. We will concentrate on uncovering structural properties of the network (e.g. density, homophily, and clustering), as well as on how to identify important persons in a network (e.g. degree centrality, structural holes, …). We will also pay attention to the detection of subgroups and deal with basic hypothesis testing for social network analysis. Throughout the course some classic theories that focus on network processes (e.g. related to homophily, centrality measures, structural holes, Granovetter’s strength of weak ties and small worlds) will be discussed.

Required Texts & Software
– Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage. (This book will be provided by the summer school on arrival).
– Borgatti, S.P., Everett, M.G., Freeman, L. 2002. UCINET 6 for Windows. Harvard: Analytic Technologies. A 30-day free trial version is available at www.analytictech.com

Additional Material
– Wasserman, S., Faust, K. 1994. Social Network Analysis: Methods and Applications. Cambridge University Press.
– Kilduff, M., Tsai, W. 2003. Social Networks and Organizations. Sage.
– Scott, J. 2000. Social Network Analysis. Newbury Park CA, Sage.
– Hanneman, R. Introduction to Social Networks. (http://faculty.ucr.edu/~hanneman/nettext/)

Topics, Readings & Exercises

Session 1. Social Network Analysis: what, how, why?

Central questions in this session:
• What is social network analysis? Why do we need social network analysis?
• How does a social network approach differ from “classic/standard” research?
• What is the difference between egocentric and complete networks?
• What type of social network data are there? How to collect social network data?
• How can we (best) visualize networks? What programs are available?

Exercises with UCINET:
• How to build/import a dataset
• Visualisation of social networks
• Collecting network data and questionnaire design

Key references:
– Chapters 7 “Vizualization”, 4 “Data Collection” and 5 “Data Management” In: Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage.
– Borgatti, S.P., Mehra, A., Brass, D.J., Labianca, G. 2009. Network analysis in the social sciences. Science 323, 892-895.
– Fisher, C.S. 1982. What do we mean by ‘friend’? An inductive study. Social Networks 3, 287-306.
– Marsden, P.V. 1990. Network data and measurement. Annual Review of Sociology 16, 435-463.
– De Lange, D., Agneessens, F., Waege, H. 2004. Asking social network questions: a quality assessment of different measures. Metodološki Zvezki – Advances in Methodology and Statistics 1, No. 2, 2004, 351-378. (http://mrvar.fdv.uni-lj.si/pub/mz/mz1.1/lange.pdf).
– McAllister, L., Fischer, C.S. 1978. Procedure for surveying personal networks. Sociological Methods and Research 7, 131-148.
– Freeman, L. 1999. Visualizing social networks. (http://www.cmu.edu/joss/content/articles/volume1/freeman.pdf).


Session 2. First analysis at the group level and at the individual level

Central questions in this session:
• What is social capital? What is social support?
• How cohesive is my network? What is network density?
• Who is most central in my network? What is degree centrality?
• When is a network centralized, and why is it important? How can we measure it?

Exercises with UCINET:
• Calculate the density of a network
• Degree centrality
• Freeman’s centralization

Key references:
– Gabbay, S.M., Leenders, R.Th.A.J. 2001. Social capital of organizations: from social structure to the management of corporate social capital. In: Gabbay, S.M., R.Th.A.J. Leenders (eds.) Research in the Sociology of Organizations Volume 18, Elsevier (pp. 1-20).
– Putnam, R. 2000. Bowling Alone: The Collapse and Revival of American Community. Simon and Schuster.
– Portes, A. 1998. Social Capital: Its origins and applications in modern sociology. Annual Review of Sociology 24, 1-24.
– Bavelas, A. 1950. Communication patterns in task-oriented groups. Journal of the Acoustical Society of America 22, 723-730.


Session 3. Centrality measures: an overview

Central questions in this session:
• What types of centrality measures are there? What is the difference between degree, closeness and betweenness centrality? What other measures of centrality are there?
• When do we use which central measure (closeness, betweenness, …)? How are they different?
• How can we deal with valued/weighted network relations?
• How can we test effects of individual position?
• What is a permutation test? Why can’t we use classic statistical tests?

Exercises with UCINET:
• Different centrality measures: closeness, betweenness, etc.
• Valued networks: centrality, density and centralization

Key references:
– Agneessens, F., Borgatti, S.P., Everett, M.G. 2017. Geodesic based centrality: Unifying the local and the global. Social Networks 49, 12-26.
– Borgatti, S.P. 2005. Centrality and network flow. Social Networks 27, 55-71.
Chapter 10 “Centrality” In: Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage.
– Freeman, L.C. 1979. Centrality in social networks: conceptual clarification. Social Networks 1: 215-239.
– Brass, D.J. 1984. Being in the right place: A structural analysis of individual influence in an organization. Administrative Science Quarterly 29, 518-539.
– Opsahl, T., Agneessens, F., Skvoretz, J. 2010. Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks 32, 245-251.


Session 4. Attributes based measures of position: Resourcefulness, diversity and social contagion

Central questions in this session:
• When can I claim that my ego-network more resourceful?
• Why do some people have more diverse ego-networks, and how does it impact their outcomes?
• What is social contagion?
• Permutation tests

Exercises with UCINET:
• Measures of diversity and Blau’s IQV
• Measures of resourcefulness
• Permutation tests

Key references:
– Mardsen, P.V., Friedkin, N.E. 1993. Network studies of social influence. Sociological Methods & Research 22, 127-151.
– Burt, R.S. 1983. Range. Pp. 176-194 in Burt & Minor (Eds.) Applied Network Analysis. Beverly Hills: Sage.
– Marsden, P.V. 1988. Homogeneity in confiding relations. Social Networks 10, 57-76.
– Campbell, K.E., P.V. Marsden, J.S. Hurlbert. 1986. Social resources and socioeconomic status. Social Networks 8, 97-117.
– van der Gaag, M., Snijders, T.A.B. 2005. The Resource Generator: social capital quantification with concrete items. Social Networks 27, 1-29.


Session 5. Structural holes, closure and brokerage roles

Central questions in this session:
• What is Granovetter’s “Strength of Weak Ties” argument? Why is it important?
• What is a “small world” network? What is six degrees of separation?
• Is it better to be connected to different groups of others, or have one big group of closely interwoven contacts? What is Ron Burt’s view? And James Coleman’s view? How can we measure this?
• What are Simmelian ties, and why are they important according to David Krackhardt?
• What are Gould and Fernandez’ brokerage types?

Exercises with UCINET:
• Clustering coefficient
• Constraint index and other measures of openness/closure
• Gould & Fernandez brokerage roles

Key references:
– Granovetter, M. S. 1973. The Strength of Weak Ties. American Journal of Sociology 78, 1360 – 1380.
– Burt, R.S. 1992. Structural Holes: The Social Structure of Competition. Harvard University Press.
– Krackhardt, D. 1999. Ties that torture: Simmelian tie analyses in organizations. Research in the Sociology of Organizations 16, 183–210.
– Gould, R., Fernandez, R. 1989. Structures of mediation: A formal approach to brokerage in transaction networks. Sociological Methodology 19, 89-126.

Session 6. Dyad and triad census

Central questions in this session:
• What is reciprocity? What is a dyad census?
• What is a random network (distribution)? How can it help to test hypotheses?
• What types of triads are there? How can I interpret different triad configurations in practice?

Exercises with UCINET:
• Dyad census and reciprocity
• Generating random graphs
• Triad census (transitivity, cyclicality, …)

Key references:
– Chapter 9 “Characterizing Whole Networks” In: Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage.
– Katz, L., J. H. Powell. 1955. Measurement of the tendency toward reciprocation of choice. Sociometry 19, 403-409.
– Skvoretz, J., F. Agneessens. 2007. Reciprocity, multiplexity, and exchange: Measures. Quality and Quantity 41, 341-357.
– Gouldner, A.W. 1960. The norm of reciprocity. American Sociological Review 25, 161-178.
– Holland, P.W., Leinhardt, S. 1970. A method for detecting structure in sociometric data. American Journal of Sociology 76, 492-513.

Session 7. Homophily and QAP regression

Central questions in this session:
• Why do friends tend to be similar to ourselves (e.g. smoking, music taste)? What is homophily? How can we measure it?
• What is QAP (multiple) regression?

Exercises with UCINET:
• Homophily (EI index)
• QAP (multiple) regression

Key references:
– Chapters 9 “Characterizing Whole Networks” and “8. Testing hypotheses” In: Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage.
– McPherson, M., Smith-Lovin, L., Cook, J.M. 2001. Birds of a feather. Annual Review of Sociology 27, 415-444.
– Krackhardt, D. 1987. QAP Partialling as a test of spuriousness. Social Networks 9, 171-186.


Session 8. Subgroups and hierarchies

Central questions in this session:
• How can I identify subgroups in my network? What types of subgroups are there? How many components does my network have? What is a clique? What is a k-plex?
• What are the properties of a hierarchical network? To what extent does my network correspond to a hierarchical network?

Exercises with UCINET:
• Subgroup analysis (components, k-cliques, k-clans, …)
• Dimensions of hierarchies

Key references:
– Chapter 11 “Subgroups” In: Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage.
– Krackhardt, D. 1994. Graph Theoretical Dimensions of Informal Organizations. In: Computational Organizational Theory. Kathleen Carley and Michael Prietula (Eds.). Hillsdale, N.J: Lawrence Erlbaum Associates.


Session 9. Equivalent positions, roles and blockmodeling

Central questions in this session:
• When do two actors have the same (or a similar) position in a network?
• What is regular equivalence? What is structural equivalence? What does it mean to be structural/regular equivalent?
• What is blockmodeling?
• What is a core-periphery structure?

Exercises with UCINET:
• Calculate structural and regular equivalence
• Identify roles through blockmodeling

Key references:
– Chapter 12 “Equivalence” In: Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage.
– Borgatti, S. P., Everett, M. G. 1992. Notions of position in social network analysis. Sociological Methodology 22, 1-35.
– Doreian, P., Batagelj, V., Ferligoj, A. 2005. Positional analysis of sociometric data. (chapter 5) In: Models and Methods in Social Network Analysis (Structural Analysis in the Social Sciences). Carrington, P., Scott, J., Wasserman, S. (eds.)
– Borgatti, S.P., Everett , M.G. 1999. Models of Core/Periphery Structures. Social Networks 21, 375-395.


Session 10. Two-mode networks

Central questions in this session:
• What is a two-mode (affiliation/bipartite) network? How is it different from a one-mode network?
• What properties of a two-mode network are interesting?
• How can we identify central persons in a two mode network?

Exercises with UCINET:
• Different ways of dealing with two-mode networks (i.e. transforming them) in order to use available procedures in UCINET.

Key references:
Chapter 13 “Analyzing Two-Mode Data” In: Borgatti, S.P., Everett, M.G., Johnson, J.C. 2013. Analyzing Social Networks. London, Sage.
– Agneessens, F., Moser, C. 2011. Bipartite Networks. In G.A. Barnett (Ed.), Encyclopedia of Social Networks (pp. 75-77). Thousand Oaks: Sage.
– Borgatti, S.P., Halgin, D. 2011. Analyzing Affiliation Networks. In Carrington, P. and Scott, J. (eds) The Sage Handbook of Social Network Analysis. Sage Publications.
– Everett, M.G., Borgatti, S.P. 2013. The dual-projection approach for two-mode networks. Social Networks 34, 204-210.
– Articles in: Agneessens, F., Everett, M. 2013. Introduction to the special issue on advanced in two-mode networks. Social Networks 35, 145-278.