Overview
This is an introductory lecture on social network analysis that I prepared in Fall 2017. It consists of an instructor introduction (Session 0) and four main sessions, and the date on the slides is October 25, 2017. The same materials are archived in three places: a Hankyung University lecture folder, a Cheonan Bugil High School lecture folder, and a social network workshop folder. At Hankyung University I gave it as a guest lecture in the Fall 2017 course “Data Analysis”. In most sessions, the explanation of concepts is followed by discussion and practice.
Contents
- Session 1. Introduction to social network analysis: the meaning of social networks and social network analysis; how to represent a network as a graph, a matrix, and an edge list; examples such as knowledge sharing, international student mobility, keyword, and co-author networks; virus spread and small-world simulations.
- Session 2. Network data structures and basic syntax: whole networks and ego networks, one-mode and two-mode networks, direction and weight.
- Session 3. Network indicators: the meaning of ties, network size and density, degree, betweenness, and closeness centrality and how they compare.
- Session 4. Network visualization: how to turn a matrix into a network with igraph in R and adjust layout and color; practice drawing example data and computing indicators in Gephi; visualizing and explaining one’s own data.
- Additional topics: inferential statistics for networks (network correlation, QAP, MRQAP, ERGM) were offered as an optional session.
Tools and materials
- R and igraph, with an R script for each session.
- Gephi.
- NetLogo web simulation models.
- Node and edge data for practice (media, airline routes, and others).
- Five slide decks built with R Markdown.