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ECPR Summer School on Methods and Techniques, 4 August – 15 10th ECPR Summer School in Methods and Techniques, 23 July - 8 August University of Ljubljana, Slovenia Course Description Form – 2 week course, 30 hours Course title SD205. Introduction to Network Analysis using Pajek Instructor details First name, last name: Vladimir Batagelj Department/Unit: Dept. of Theoretical Computer Science Institution: Institute of Mathematics, Physics and Mechanics Phone: +386 1 4766-672 Fax: +386 1 2517-281 E-mail : [email protected] 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. Long course outline Course description: The course will start with an overview of the history of social network analysis, followed by a presentation of some typical and well-known real-life networks. In the main part fundamental concepts and methods of network analysis will be explained. Lab sessions will be performed using the software package Pajek. Then the course will cover the following topics: 1) Basic network concepts: network representations: matrix, graph; types of networks: undirected networks, directed networks, multi-relational networks, 2-mode networks, temporal networks; size and density; small, large and huge networks, sparse and dense networks; 2) Program Pajek and other network analysis software: description of networks in Pajek input file; network layouts: automatic and manual drawing; Unicode; connection with statistical packages (R); utility programs: Text2Pajek, GSView, SVG, King, Inkscape; 3) Paths in networks: walk, chain and path; closed walk, cycle, closed chain, loop; length and value of path; the shortest path, diameter; k-neighbours; acyclic networks; 4) Centrality: degree, closeness, betweenness; hubs and authorities, clustering coefficient; Hummon-Doreian’s weights in acyclic networks; small world and scale-free networks; 5) Weights and properties: line and vertex cuts, sub-networks; regression; visualisation in Pajek; 6) Connectivity: weakly, strongly and bi-connected components; global and local views; contraction; extraction; skeletons: minimal spanning trees, Pathfinder; 7) Cohesion: triads, cliques, rings, cores, islands; strong and weak ties; pattern search (motifs); 8) 2-mode networks: examples of 2-mode networks; direct analysis of 2-mode networks; multiplication of networks; transforming 2-mode to 1-mode networks; analysis of bibliometric (citation, collaboration, keywords/tags,...) networks; 9) Blockmodelling: direct and indirect approaches; structural, regular equivalence; 10) Generalised blockmodelling and blockmodelling of 2-mode networks; 11) Temporal, spatial and multirelational networks: macros and operations on sequences of networks. After listening to the lectures, participants will work individually in computer labs. Several data sets will be prepared to challenge their knowledge. Day-to-day schedule (Monday 27 July – Friday 7 August) Topic(s) Details Day 1 Monday Mix (90 min general Introduction to the course. Networks. Pajek. introduction to the topic) Example. Day 2 Basic network concepts Partitions and vectors. Visualisation. Types of networks. Pajek and network analysis software. Day 3 Local and global views Sub networks. Cuts. Paths in networks. Day 4 Connectivity Connectivity types. Acyclic networks. Short cycles and Granoveter’s week ties. Day 5 Centrality and prestige Measures. Hubs and authorities. Triads. Pattern search (motifs). Day 6 Cohesion Cliques, cores, generalised cores, islands. Day 7 2-mode networks Derived 1-mode networks. Direct methods: 4-rings, 2-mode cores. Day 8 Clustering and blockmodeling Indirect and direct approaches. Day 9 Multiplication of networks Networks from the tables. Temporal, multirelational and sequences of networks. Day 10 Network statistics Scale-free networks Each meeting consists of a 90 minutes lecture, and a 90 minutes lab session. The concepts explained are applied in Pajek during the lab sessions. Students will receive test datasets to practice. An assignment will be handed out each day after the lectures. The students are expected to return an individual report next day to TA. The exam is open book and will consist of a set of questions to be answered by short answers. The final grade = 0.6 X assignments + 0.4 X exam. Day-to-day reading list Instead of readings an assignment will be handed out each day after the lectures. The students are expected to return an individual report next day to the TA. Working on the assignment, they are expected to consult the available materials (slides from the lectures, books). Software and hardware requirements Software programme Program Pajek with supporting tools BabelPad, Gsview/Ghostscript, Acrobat Reader, Inkscape, SVGviewer, King, instantreality X3D player, IrfanView, R, Text2Pajek, WoS2Pajek. Hardware requirements Standard PCs. Literature The course will be based on chapters 2 and 3 from the book: V. Batagelj, P. Doreian, A. Ferligoj and N. Kejžar: Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution. Wiley Series in Computational and Quantitative Social Science. Wiley, 2014. and the book W. de Nooy, A. Mrvar, V. Batagelj: Exploratory Social Network Analysis with Pajek, CUP. Second expanded edition 2011. Amazon. See also the articles V. Batagelj: Social Network Analysis, Large-scale V. Batagelj: Visualization of Large Networks W. deNooy: Social Network Analysis, Graph Theoretical Approaches to published in: Encyclopaedia of Complexity and Systems Science. (Meyers, Robert A., Ed.), Springer, Heidelberg 2009; and V. Batagelj: Large-Scale Network Analysis. J Scott, P Carrington (eds): The SAGE Handbook of Social Network Analysis, SAGE, 2011. There are other books on network analysis S. Wasserman, K. Faust: Social Network Analysis: Methods and Applications. CUP, 1994. Amazon. P.J. Carrington, J. Scott, S. Wasserman (Eds.): Models and Methods in Social Network Analysis. CUP, 2005. Amazon. J. P Scott: Social Network Analysis: A Handbook. SAGE Publications, 2000. Amazon. Degenne, M. Forsé: Introducing Social Networks. SAGE Publications, 1999. Amazon. P. Doreian, V. Batagelj, A. Ferligoj: Generalized Blockmodeling, CUP, 2004. Amazon. D. Easley, J. Kleinberg: Networks, Crowds, and Markets. CUP, 2010. Amazon M.O. Jackson: Social and Economic Networks. PUP, 2010. Amazon. Some papers: L.C. Freeman 1979: Centrality in Social Networks: A Conceptual Clarification. Social Networks 1: 211-213. M. Granovetter 1973: The Strength of Weak Ties. American Journal of Sociology 78: 1360-80. Science: Complex Systems and Networks, 24 July 2009, Vol 325, Issue 5939, Pages 357-504. N. Kejžar, S. Korenjak-Černe, V. Batagelj: Network Analysis of Works on Clustering and Classification from Web of Science, in Proceedings of IFCS'09. V. Batagelj, M. Cerinšek 2013: On bibliographic networks. Scientometrics, Volume 96, Issue 3, pp 845-864. Additional materials will be available at the course wiki page http://pajek.imfm.si/doku.php?id=event:ecpr15 Lecture room requirement Classical lecture room + computer lab. Preferred time slots Morning .
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