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Theoretically Informed Network Analysis for Social Scientists

Balázs Vedres

Central European University

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.


Course Dates and Times

Monday 1 to Friday 5 August 2016
Generally classes are either 09:00-12:30 or 14:00-17:30
15 hours over 5 days

Prerequisite Knowledge

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).

Short Outline

The aim of this course is to enable participants to formulate and test their own theoretical arguments related to social network phenomena. The course will provide examples of network arguments from key writings in the social science, and will also provide hands-on introduction to the methods used in these examples. The course will cover arguments about such topics as network diversity and creativity, brokerage and managerial success, cohesion and political corruption, multiplexity and power, or the transformation of the economic world system. Participants will learn to use software packages (primarily Ucinet, and Gephi), and will obtain skills to collect, manage, analyze, and visualize their own network data.

Long Course Outline

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 center on strategic agents exploiting openings in their network structures. 


This course aims at helping the participants 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 0.8 for analyzing and visualizing network data.  We will use Excel for basic data entry and manipulation tasks, although students are encouraged to explore Python, R, or Matlab tools for data collection and manipulation. Pointers for exploring such solutions will be offered in the beginning of the course.


The first argument that we will consider, as an introduction to working with social networks data, is about the diversity of the nodes connected.  Is diversity related to creative success?  During the first day, participants will learn the basic skills of recording and manipulating dyadic data, and the basics of visualizing network data with node attributes. We will discuss fundamental graph theory concepts, such as degree, path, and distance.  We will also introduce the basics of using graph visualizations, such as spring embedders, circular layouts, node and edge size and color settings, and filterings.


The second day will be about arguments centered on the node level, primarily arguments about the advantages to brokerage (the unique vantage point that 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 we will visualize the diverse network reach of brokers.  We will also introduce hypothesis testing at the node level, addressing non-parametric methods appropriate for interdependent observations.


The third day will be about the causal significance of cohesion, illustrated through the case of political corruption in business networks. We will introduce methods to measure cohesion, and to identify cohesive clusters, and we will discuss ways to visualize cohesive groups.  We will discuss the advantages and disadvantages to methods that identify exclusive or overlapping clusters. We will discuss the basics of hierarchical cluster analysis, and modularity as a diagnostic.


The fourth theme will be about network multiplexity and power, introducing structural equivalence blockmodels of multiple networks.  We will discuss methods of finding and visualizing structural equivalence blocks, and we will introduce goodness of fit measures.  We will introduce the idea of working with block images, and relational multiplication tables.  We will also talk about the significance and challenges of working with multiplex data.


The fifth and final theme will be about the dynamics of the economic world system, through the networks of international and intersectoral trade. We will discuss valued networks, cutoffs, filterings. We will introduce regular equivalence blockmodels, and methods to analyze and visualize networks through time.  

Day Topic Details
Monday Basics; arguments for diversity.

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

Tuesday Node level measures; arguments for brokerage.

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

Wednesday Cohesive groups.

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

Thursday Multiplex networks; generating power.

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

Friday Dynamic networks.

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

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


Gephi 0.8

(both run on Windows 7, 8.x, 10)

Hardware Requirements

Participants need to bring their own laptop. With the datasets supplied in class, basic PC software is sufficient.


Barabasi, Albert Laszlo. 2002. Linked: The New Science of Networks. Perseus Books Group: New York.

Diani, Mario, and Doug McAdam. 2003. Social Movements and Networks. Oxfor University Press:Oxford, UK.

Hennig, Marina et al. 2012. Studying Social Networks. Campus Verlag:Frankfurt Hennig, Marina et al. 2012. Studying Social Networks. Campus Verlag:Frankfurt.

Knoke, David. 1990.  Political Networks: The Structural Perspective. Cambridge University Press: Cambridge UK.

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

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

Additional Information


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.