Social Network Analysis for Computer Scientists

About social network analysis tools and packages. There exist different tools and packages for social network analysis. In this course, we cover two of them in this course, with complementary advantages:

1. Gephi, an easy-to-use tool with a graphical interface useful for visualization and quick analysis of relatively small network data (this week, see below).
   
2. NetworkX, an extensive Python package for network analysis that can handle larger network datasets and computations (next week).

Learning goals. The main goal of this lab session is to become familiar with Gephi (experimental beta-software to visualize networks for research purposes) and its input format. At the end of this session you should be able to:

• Know how to use Gephi for social network analysis
• Import and visualize raw network data with labeled nodes and labeled and/or weighted edges (directed or undirected),
• Understand how to map edge and node size and color to structural network properties such as the node degree and edge type,
• Know how to apply filters to the visualization, for example to focus only on the giant component,
• Export a vector graphic PDF of your network for reuse in for example a presentation or paper,
• Export computed node data for reuse in another program.

There is no deliverable for this lab session, but you are assumed to know the tool afterwards. Practice more at home if needed.

Instructions for today: Walk through the complete Gephi tutorial.

Note that the tutorial briefly covers topic such as centrality and communities, that will not be covered extensively until Lecture 2.

Done? Get started with the practical part of Assignment 1. You can download the smaller datafiles medium.tsv and large.tsv. If you want to analyze huge.tsv, you will have to get it from the shared folder in the ISSC Linux or LIACS DS lab environment, as stated in the assignment.