This course is now full, and we are operating a waiting list. Please complete an application form if you would like to be added to the waiting list.

 

Please note: This course will be taught in hybrid mode. Hybrid delivery of courses will include synchronous live sessions during which on campus and online students will be taught simultaneously.

Filip Agneessens is an Associate Professor at the Department of Sociology and Social Research, University of Trento. He has published on a diversity of topics related to social networks, including measures of centrality, statistical models, ego- networks and social support, two-mode networks, negative ties, multilevel networks and issues related to data collection. He has also applied social network analysis to understand the antecedents and consequences of interactions among employees, and in particular within teams. Together with Martin Everett, he was a guest editor for a special issue on “Advances in Two-mode Social Network Analysis” in the journal Social Networks, and together with Nick Harrigan and Joe Labianca he guest-edited a special issue on “‘Negative and Signed Tie Networks”’. He has taught numerous introductory and advanced social network courses and workshops over the last 15 years. Together with Steve Borgatti, Martin Everett and Jeff Johnson he co-authored the book “Analyzing Social Networks with R” (Sage, 2022).

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 social network packages in R, although most exercises can also be performed with 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 Pre-requisites

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 – t
his text will be provided by ESS:

Borgatti, S. P., Everett, M. G., Johnson, J. C., & Agneessens, F. (2022). Analyzing Social Networks Using R. SAGE.

Background knowledge required
Maths

Linear Regression = elementary

POTENTIAL ESS APPLICANTS ARE TO BE ADVISED THAT RECORDINGS WILL NOT BE MADE AVAILABLE FOR THIS COURSE.

For participation in this course, students are required to bring with them their own laptops.

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 R. 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 Textbook

Borgatti, S. P., Everett, M. G., Johnson, J. C., & Agneessens, F. (2022). Analyzing Social Networks Using R. SAGE. 

Required Software

R and RStudio

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?
• How can we (best) visualize networks? What programs are available?

Lab exercises:

  • How to build/import a dataset in R
  • Visualisation of social networks in R

 

Core Reading:

– Chapter 7 “Visualization” and Chapter 5 “Data Management” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

Background reading:

– Borgatti, S.P., Mehra, A., Brass, D.J., Labianca, G. 2009. Network analysis in the social sciences. Science 323, 892-895.

Session 2.

First analysis at the group level and at the individual level

Central questions in this session:
• What type of social network data are there? How to collect social network data?
• 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 this?

Lab exercises:


• How to calculate the density of a network, the degree centrality and Freeman’s centralization

  • Reflect on types of network data

 

Core Reading:

– Chapter 4 “Data Collection” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

– Agneessens, F., & Labianca, G. J. (2022). Collecting survey-based social network information in work organizations. Social Networks, 68, 31-47.

Background reading:

– 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.

– McAllister, L., Fischer, C.S. 1978. Procedure for surveying personal networks. Sociological Methods and Research 7, 131-148.

– 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 are:

  • What is the difference between degree, closeness and betweenness centrality?
  • What is the difference between eigenvector and beta centrality? And which values can we choose for beta? What is the meaning of beta centrality when beta is negative?
  • What other measures of centrality are there? And when do we use which central measure?
  • How can we deal with valued/weighted network relations? And what if we are dealing with negative or signed networks?

 

Lab exercises:


• How to calculate the different measures of centrality using R, and how to interpret the results

Core Reading:

– Chapter 9 “Centrality” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

Background reading:

– Freeman, L.C. 1979. Centrality in social networks: conceptual clarification. Social Networks 1: 215-239.

– Borgatti, S.P. 2005. Centrality and network flow. Social Networks 27, 55-71.

– Agneessens, F., Borgatti, S.P., Everett, M.G. 2017. Geodesic based centrality: Unifying the local and the global. Social Networks 49, 12-26.

– 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.

Structural holes, closure and brokerage roles

Central questions in this session are:

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

 

Lab exercises:


• How to calculate structural holes and local structural measures using R

Core Reading:

– Chapter 8 “Local Node-Level Measures” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

Background reading:

– 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.

– Watts, D. J. 2000. Small worlds: The dynamics of networks between order and randomness. Princeton: Princeton University Press.

Session 5.

Attributes based measures of position:

Resourcefulness, diversity and homophily

Central questions in this session:

  • When can I claim that my ego-network is more resourceful?
  • Why do some people have more diverse ego-networks, and how can I measure this?
  • How can I test whether network positions have an impact on nodal outcomes? What is a permutation test? How is it different from a classic statistical tests?
  • Do friends tend to be similar to ourselves (e.g., smoking, music taste)? What is social contagion? What is an autoregressive model?

 

Lab exercises:


• How to perform a permutation test with R

  • How to interpret an autoregressive model

 

Core Reading:

– Chapter 8 “Local Node-Level Measures” and Chapter 14 “Introduction to Inferential Statistics for Complete Networks” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

Background reading:

– 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.

– Christakis, N. A., & Fowler, J. H. (2013). Social contagion theory: examining dynamic social networks and human behavior. Statistics in Medicine, 32(4), 556-577.

Session 6.

Cohesion, dyad census and triad census

Central questions in this session:
• How can we measure cohesion in a network? What is the compactness of a network?

  • What is reciprocity? What is a dyad census?
  • What types of triads are there? How can I interpret different triad configurations in practice?
  • What is a random network (distribution)? How can it help to test hypotheses whether there is a more-than-chance level of reciprocity or clustering/transitivity in a network?

 

Lab exercises:


• Cohesion measures

  • Dyad census and reciprocity
    • Generating random graphs
    • Triad census (transitivity, cyclicality, …)

 

Core Reading:

– Chapter 10 “Group-level measures” and Chapter 14 “Introduction to Inferential Statistics for Complete Networks” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

 Background reading:

– 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, QAP regression and a short introduction to other statistical models

Central questions in this session:
• How can we test whether a network exhibits an “above chance” level of homophily? Or whether girls have more friends than boys in a school class?
• How does QAP (multiple) regression work? And how is it different from ERGM?

Lab exercises:


• How to test for homophily in a network

  • How to perform a QAP regression analysis

 

Core Reading:

– Chapter 14 “Introduction to Inferential Statistics for Complete Networks” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

Background reading:

– 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 community detection

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?
• How can we identify “communities” in a network?

Lab exercises:


• How to identify subgroups in a network (components, k-cliques, k-clans, …)

  • How to perform community detection (Girvan-Newman, Louvain method, …)

 

Core Reading:

– Chapter 11 “Subgroups and community detection” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

Background reading:

– Girvan M, Newman ME (2002) Community structure in social and biological networks. PNAS 99(12):7821–7826.

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? What are the properties of a hierarchical network? To what extent does my network correspond to a hierarchical network

Lab exercises:


• Calculate structural and regular equivalence and perform analysis on these
• Identify roles through blockmodeling

Core Reading:

– Chapter 12 “Equivalence” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

Background reading:

– 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.

– 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 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?
  • How can we identify subgroups in a two-mode network?

 

Lab exercises:


• Visualize and transform a two-mode network into a one-mode network or bipartite network in order to perform analyses on them with R

Core Reading:

– Chapter 13 “Analyzing two-mode data” In: Borgatti, S.P., Everett, M.G., Johnson, J.C., Agneessens, F. 2022. Analyzing Social Networks with R. London, Sage.

Background reading:

– 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.

– Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social networks19(3), 243-269.