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Social Networks: Theoretically Informed Analysis with UCINET

Course Dates and Times

Monday 29 July – Friday 2 August

09:00–10:30 and 11:00–12:30

Balázs Vedres

Central European University

The aim of this course is to enable you to formulate and test your own theoretical arguments related to social network phenomena.

The course will provide examples of network arguments from key writings in the social sciences, and is a hands-on introduction to the methods used in these examples.

We will cover arguments about network diversity and creativity, brokerage and managerial success, cohesion and political corruption, multiplexity and power, or the transformation of the economic world system.

You will learn to use the software package UCINET, and some Gephi, and will gain the skills to collect, manage, analyse, and visualise your own network data.

ECTS Credits for this course and below, task for additional credits:

1 additional credit Complete a take-home paper of 2,000–3000 words, testing a network hypothesis with data, with a visualisation of the network, and a statistical test of the hypothesis. Datasets and possible research questions will be made available in the course.

2 additional credits As above, plus complet two assignments, handing in outputs (one page each).

Instructor Bio

Balázs Vedres' research furthers the agenda of understanding historical dynamics in network systems, combining insights from network science, historical sociology, and studies of complex systems in physics and biology.

His contribution is to combine historical sensitivities to patterns of processes in time with a network analytic sensitivity to patterns of connectedness cross-sectionally. A key element of this work was the adoption of optimal matching sequence analysis to historical network data.

Balázs' research has been published in top journals of sociology, with two recent articles in the American Journal of Sociology exploring the notion of structural folds: creative tensions in intersecting yet cognitively diverse cohesive communities. His recent research follows video game developers and jazz musicians as they weave collaborative networks through their projects and recording sessions.

He is the recipient of several awards and prizes, and is the founder and director of CEU's Center for Network Science.


Social network analysis uses dyadic data (data about the connections between entities) to construct measures of larger structures, such as cohesive groups, bridging individuals, unique positions, or central and peripheral actors. In more general terms, social network analysis is a paradigm that builds theoretical arguments about the impact of triadic or higher order (meso-level) structures on individual nodes, dyads, or macro-level systems. This paradigm is closely related to the relational turn in the social sciences, and provides us with the conceptual tools to avoid falling into traps of structuralism, to formulate arguments that centre on strategic agents exploiting openings in their network structures. 

This course aims to help you build and test such arguments, with the help of network data. Each day will focus on a particular argument, with a reading and hands-on work with data. The readings, software, and data will be provided by the Instructor. We will primarily use UCINET 6, and Gephi 0.9 for analysing and visualising network data. We will use Excel for basic data entry and manipulation tasks.

The first argument we will consider, as an introduction to working with social network data, is about the diversity of the nodes connected. Is diversity related to creative success? 

Day 1
You will learn the basic skills of recording and manipulating dyadic data, and the basics of visualising network data with node attributes. We will discuss fundamental graph theory concepts, such as degree, path, and distance. We also introduce the basics of graph visualisations, such as spring embedders, circular layouts, node and edge size and colour settings, and filterings.

Day 2
This will be about arguments centered on the node level, primarily arguments about the advantages to brokerage (the unique vantage point actors enjoy when they are linked to distinct regions of the network). We will introduce competing measures of nodal prominence (centrality, prestige, and brokerage measures), and visualise the diverse network reach of brokers. We also introduce hypothesis testing at the node level, addressing non-parametric methods appropriate for interdependent observations.

Day 3
The causal significance of cohesion, illustrated through the case of political corruption in business networks. We introduce methods to measure cohesion, and to identify cohesive clusters, and discuss ways to visualise cohesive groups. We also discuss the advantages and disadvantages of methods that identify exclusive or overlapping clusters; the basics of hierarchical cluster analysis, and modularity as a diagnostic.

Day 4
Network multiplexity and power, introducing structural equivalence block-models of multiple networks. We discuss methods of finding and visualising structural equivalence blocks, and introduce goodness of fit measures. We introduce the idea of working with block images, and relational multiplication tables, and talk about the significance and challenges of working with multiplex data.

Day 5
Dynamics of the economic world system, through the networks of international and intersectoral trade. We discuss valued networks, cutoffs, and filterings. Finally, we introduce regular equivalence block-models, and methods to analyse and visualise networks through time. 

Basic knowledge of statistics and probability (probability density functions, tests of statistical significance, ordinary least squares estimator).

Basic knowledge of data entry and manipulation (for example using Excel sheets).

Day Topic Details
1 Basics; arguments for diversity.

Introduction to network data; the basics of network analysis and visualisation; the advantages to network diversity.

2 Node level measures; arguments for brokerage.

Working with node level measures, such as centrality, prestige, brokerage.

3 Cohesive groups.

Measuring cohesion and identifying communities. The case of political corruption in business networks.

4 Multiplex networks; generating power.

Working with multiplex network data; structural equivalence blockmodels; block images and multiplication tables.

5 Weighted and dynamic networks.

Working with valued networks; world system networks via regular equivalence blocking; dynamic visualisations.

Day Readings

Hennig, Marina et al. 2012. Studying Social Networks. Campus Verlag: Frankfurt. chapters 1 and 2.


Burt, Ronald. 1995. Structural Holes: The Social Structure of Competition. Harvard University Press: Cambridge MA.  chapters 1 and 2.


Wasserman, Stanley, and Katherine Faust. 1994. Social Network Analysis. Cambridge University Press: Cambridge UK. chapter 7


Padgett, John and Christopher K. Ansell. 1993. Robust Action and the Rise of the Medici 1400-1434. American Journal of Sociology 98(6):1259-1319.


Scott, John, and Peter J. Carrington (editors). 2011. The Sage Handbook of Social Network Analysis. Sage:London, UK. chapter 22.

Software Requirements

We will use UCINET 6, available from This requires a computer with Windows, or a machine that runs Windows as a virtual machine.

Hardware Requirements

Please bring your own laptop.

  • Windows operating system Vista or later. If you have a Mac or Linux, you can run UCINET via BootCamp, VMFusion Ware, Parallels or Wine. See UCINET site FAQ on this.
  • The 32-bit version is the standard one and runs on both 32bit and 64bit Windows systems. A limited 64-bit version is available but does not have all UCINET functions
  • 100mb of disk space for the program itself (not including your data)
  • The more RAM the better, but the 32-bit version can't take advantage of more than 3GB of memory. If you have large data and a 64-bit version of Windows, you can try experimental 64-bit version, in which case 8GB of RAM or more would be useful. Remember, however, that even if a really large dataset fits in memory, it may take too long to analyse.
  • While the absolute maximum network size is about 2 million nodes, in practice most UCINET procedures are too slow to run networks larger than about 5,000 nodes. However, this varies depending on the specific analysis and the sparseness of the network. For example, degree centrality can be run on networks of tens of thousands of nodes, and most graph theoretic routines run faster when you have very few ties, no matter how many nodes you have.


Useful reading

Wasserman, Stanley, and Katherine Faust. 1994. Social Network Analysis. Cambridge University Press: Cambridge.

Recommended Courses to Cover After this One

Advanced Social Network Analysis and Visualisation with R