Short Bio
Vladimir Batagelj is professor of Discrete and Computational Mathematics at the University of Ljubljana. His main research interests are in mathematics and computer science: combinatorics with emphasis on graph theory, algorithms on graphs and networks, combinatorial optimisation, algorithms and data structures, cluster analysis, visualisation, social network analysis and applications of information technology in education. With Andrej Mrvar he is developing from 1996 a program Pajek for analysis and visualisation of large networks. He is a co-author of the books Exploratory Social Network Analysis with Pajek (Cambridge University Press, 2005; Second edition, 2011), Generalized Blockmodeling (Cambridge University Press, 2004) and Understanding Large Temporal Networks and Spatial Networks (Wiley, 2014).
http://vladowiki.fmf.uni-lj.si/doku.php?id=vlado
Prerequisite knowledge
Note from the Academic Convenors to prospective participants: by registering to this course, you certify that you possess the prerequisite knowledge that is requested to be able to follow this course. The instructor will not teach again these prerequisite items. If you doubt whether you possess that knowledge to a sufficient extent, we suggest you contact the instructor before you proceed to your registration.
Participants need to have basic knowledge of mathematics (set theory notation, computation with matrices and vectors) and statistics. Basic familiarity with at least one statistical package (R or SPSS) can be helpful. Participants are expected to attend computer labs daily, where the software package Pajek will be used. During the lab hours, students will perform several network analyses on different small and large networks individually. Basic computer literacy (text editor, spreadsheet tool (Excel), word processing tool (Word or LaTeX), archiving tool (ZIP)) is expected.
Short course outline
The course aims to provide an introduction into the main topics and concepts of social network analysis. It focuses on the analysis and visualisation of complete networks. Participants will get an understanding of basic network analysis concepts like centrality, cohesion, blockmodeling, etc. Special attention will be given to the analysis of large networks. After the course participants should be able to examine data in ’social networks way’ – they should be able to identify and formulate their own network analysis problems, solve them using network analysis software and interpret the obtained results. The course is supported by Pajek – a program for analysis and visualisation of large networks.
Instructor Bio
Silvia Fierascu is a PhD candidate in Comparative Politics and Network Science at Central European University. Her research focuses primarily on quality of governance, political-business relations, and statistical analyses of network data. Silvia is involved in various interdisciplinary projects, translating complex problems into applications for organizational management, real-time political communication, and better governance.
