balazs vedres

I am a network scientist, founder and director of the Center for Network Science and associate professor at the Department of Sociology and Social Anthropology, at Central European University.

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My research furthers the agenda of understanding historical dynamics in network systems, combining insights from network science, economic sociology, historical sociology, and studies of complexity in physics and biology. My research findings were published in the top journals of sociology, with two recent articles in the American Journal of Sociology exploring the concept of structural folds: creative tensions in intersecting yet cognitively diverse cohesive communities. For the past ten years I was building an interdisciplinary center, the Center for Network Science at CEU, that is now becoming a department. I was building up the second PhD program in network science in the world. I am also an entrepreneur, co-founder of digital startups offering SaaS solutions based on network science and gamification. I had been translating my research outcomes into consulting for several business and government entities for the last twenty years.

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My most recent research follows video game designers, jazz musicians, and software developers as they weave collaborative networks through their projects, recording sessions, repositories. I am answering questions about the sources of creativity, the significance of gender diversity, and the nature of gender inequality. At a more general level I am investigating the historical sustainability of innovation systems. I am also engaged in research that explores the networked evolution of the global economy in a monograph in the works for Cambridge University Press titled "Network Science of Economic Globalization."

Blog: Jazz bands succeed by missing links

Balazs Vedres.
"Forbidden triads and creative success in jazz: the Miles Davis factor."
Applied Network Science.

This article argues for the importance of forbidden triads – open triads with high-weight edges – in predicting success in creative fields. Forbidden triads had been treated as a residual category beyond closed and open triads, yet I argue that these structures provide opportunities to combine socially evolved styles in new ways. Using data on the entire history of recorded jazz from 1896 to 2010, I show that observed collaborations have tolerated the openness of high weight triads more than expected, observed jazz sessions had more forbidden triads than expected, and the density of forbidden triads contributed to the success of recording sessions, measured by the number of record releases of session material. The article also shows that the sessions of Miles Davis had received an especially high boost from forbidden triads.

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The aim of this course is to give scalable computational tools to researchers interested in investigating social network research questions. The course will introduce R (via R notebooks) as a key tool to analyze networks: to import and manage network data, to describe a network, to visualize networks from a dozen to tens of thousands of nodes, and to use statistical tests for network hypotheses at node or dyadic levels. We will discuss centralities, community detection, blockmodeling, brokerage, rewiring-based null models, and multiplex networks. Datasets from diverse research projects will be provided: collaboration and communication networks, world trade, animal social networks.

You are expected to download and install R-studio, and bring your computer to class. We will step through procedures together in class. Please see the resources below for R notebook files that you can open in R-studio. Data will also be there. You should download these and keep them in the same folder. So first, make a folder for the class. Make sure you have read-write privileges for this folder.

Then install R-studio from here: https://www.rstudio.com/products/rstudio/download/

Then download R notebooks, and un-zip the contents in your folder for the class. These are R notebooks (RMD files). You can open the ".Rmd" files in R-studio. The RMD file names tell you the sequential order of the class, and also the theme of the notebook.

And last, download data files and helper files we will need, and un-zip the contents in your folder for the class. These are various text, CSV and R code files that our R notebooks will be using.

Email
vedresb@ceu.edu

Adress
Nador utca 9
Budapest, 1051
Hungary.

Phone
+36 1 327 3000

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