Social Network Analysis

Key information

Start date: 04 May 2020

Attendance dates:

May 2020: 04, 05, 06

Time commitment: 3 days


Stag Hill campus, University of Surrey, Guildford, Surrey GU2 7XH

Contact details:


Social network analysis helps us understand individuals’ contact with the larger social world. It focuses on relationships between social entities, and analyses patterns of social interaction and their influence on individual behaviour.

This module will introduce you to various concepts, methods and applications of social network analysis drawn from the social sciences. You will begin with an introduction to graph theory and the fundamentals of social network analysis, including data collection and visualisation. You will then consider descriptive network-level and individual-level statistics and their applications in social science research. Finally, you will discuss methods for testing hypotheses about social network structure and introduce models for social networks. The emphasis will be on applying social network analysis theories and methods to real-world data, and on understanding and interpreting results, rather than on the underlying mathematics.

Learning outcomes

On successful completion of this module, you will be able to:

  • Describe social network analysis concepts, data collection strategies, and analytic techniques (C and K)
  • Have a critical understanding of the key network data collection strategies and their potential limitations (C and K)
  • Use social network analysis statistics and models to describe social networks and test hypotheses, and interpret the results (C and P)
  • Be able to implement a social network analysis on real world data and critically evaluate the results (C and P)
  • Engage with different applications of social network analysis in the social sciences (C and T)
  • Use social network analysis software to analyse network data (P and T)


Code Description
C Cognitive/analytical
K Subject knowledge
P Professional/practical skills
T Transferable skills

Course content

  • Historical and theoretical foundations
  • Data sources and data collection strategies
  • Graphs, matrices, and sociograms
  • Centrality and centralisation
  • Balance, reciprocity, and transitivity
  • Density and cohesive subgroups
  • Equivalence and blockmodels
  • Dyads and triads
  • Statistical models for social networks

Learning and teaching methods

  • Lectures
  • Practical workshops
  • Group discussion

Course leader

Giulia Berlusconi

Giulia is interested in fusing criminology scholarship with quantitative methodologies and in particular, social network analysis. Her research focuses primarily on co-offending and illicit markets.

Reading list

Borgatti, S.P., Mehra, A., Brass, D.J. and Labianca, G. (2009). Network Analysis in the Social Sciences. Science, 323, pp.892-895.

Software and equipment

All computing workshops will be in R, using RStudio. For a basic introduction to R for data manipulation and analysis, see our interactive workshops.

Entry requirements

There are no formal entry requirements for this module.

Fees and funding

Price per person:


Government and commercial sector applicants


Education and charitable sector applicants



How to apply

You can apply for this module through our online store.

Apply now

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