All programmes > Network Analysis

Network Analysis (B-KUL-G0W14A)

6 ECTSEnglish30 Second term
N.
This course is not taught this academic year, but will be taught next year. This course is not taught this academic year, but will be taught next year.
POC Sociologie

Aims

This course covers general topics of network analysis. It offers introduction to network analysis and overview from well established to recently developed methods in connection with specific social theories.

Students will be trained to:
1. Understand types of questions specific network analysis methods are able to answer and what are their limitations.
2. Link the methods to social theory: examples of applications of specific network analysis techniques used to answer specific questions in social sciences.
3. Use of palette of available computer software for network analysis.
4. Implement techniques through a number of tasks to a new datasets in order to implement techniques independently
5. Choose the appropriate technique and selecting proper model depending on research question and also control the assumptions for the model are met.
6. Report results in a clear manner in the papers.
 

Previous knowledge

Basic knowledge of statistical methods in the social sciences are helpful.  This corresponds to the level provided by From Problem to Analysis.

Is included in these courses of study

Activities

6 ects. Network Analysis (B-KUL-G0W14a)

6 ECTSEnglishFormat: Lecture30 Second term
N.
POC Sociologie

Content

Week 1: Introduction to Social Network Analysis (Luka Kronegger)
1. Classic research vs. network research
2. Definitions, introduction to graph theory, introductory network analysis
3. Understanding of concepts  for analysing relations combined with personal attributes:

  • to investigate structural processes (e.g. tendency of reciprocated trust or friendship transitivity)
  • influence processes (e.g. effect of position in the network to specific outcome - diffusion of information)
  • social selection (e.g. homophily)

4. Data sources, gathering and preparation

  • delineating a network
  • network boundaries
  • network surveys

5. Methods  for SNA

  • basic methods
  • centrality measures
  • openness and closure
  • global measures, clustering,
  • resources and attitudes

Week 2: Multivariate models for cross-sectional social network data (András Vörös)
1. social positions and roles

  • structural equivalence
  • blockmodeling

2. Exponential Random Graph Models (ERGMs)

Week 3: Multivariate models for longitudinal social network data (András Vörös)
1. the Stochastic Actor-oriented Model (SAOM) for network dynamics

  • modeling the dynamics of a single social network

2. network-behavior co-evolution: identifying selection and influence processes
3. the co-evolution of multiple social networks

 

 
 

Course material

Recommended reading:
• Wasserman and Faust. 1994. Social Network Analysis.
• de Nooy, Mrvar and Batagelj. 2011. Exploratory Social Network Analysis with Pajek.
• RSiena webpage. https://www.stats.ox.ac.uk/~snijders/siena/
• ERGM tutorial. http://statnet.csde.washington.edu/workshops/SUNBELT/EUSN/ergm/ergm_tutorial.html

Software:
• PAJEK, Program package for analysis and visualization of large networks. Pajek can be downloaded for free from http://pajek.imfm.si/

• R, a free software environment for statistical computing and graphics with additional contributed packages Network,sna, ERGM, RSiena and others. Info on R and contributed packages: https://www.r-project.org/

Format: more information

Lectures and computerlab exercises

This course module is taught in block teaching. All contact moments are concentrated in four weeks, with three lectures a week, all scheduled in the evening and on Saturday. 

Evaluation

Evaluation: Network Analysis (B-KUL-G2W14a)

Type : Exam outside of the normal examination period

Explanation


Examination on the basis of 3 small papers (exercises)