Social Network Analysis for Computer Scientists

The main goal of this session is to get to know the ANS system for digital examination, and practice with more theoretical aspects of the course.

Navigate to ANS, and follow the instructions there.

The main goal of this lab session is to become familiar with NetworkX (a Python package to analyze networks for research purposes).
All relevant information on NetworkX can be found in the NetworkX online documentation.

Your Python environment

For this lab session, you need a working Python environment. For this, there are two options:

1. Use your own self-installed Python environment, and choose your own editor and way of running code (via the command line, an IDE or an interactive notebook). Proceed to Instructions for today
2. Alternatively, you can use the desktop machines in the student computer rooms. One easy way to create a Python environment on the university computers, is to use conda. As of 2024, conda appears to be installed already. Therefore, you can likely skip step 1--3 below, and start at step 4.

4. Create a new conda environment:
   conda create --name snacs
   And activate it with:
   conda activate snacs
   You might use conda for other courses as well, and version conflicts can arise when you install many packages. Using separate environments prevents this.
   NOTE: some packages can make your conda environment take up a lot of space. So try not to use an unnecessary number of environments with duplicate packages. You can also remove an environment that you don't need anymore, see the conda cheatsheet.
5. Packages can easily be installed with conda. To install NetworkX, run:
   conda install networkx
6. Getting started
   • Running code from the command line. Now you should be able to run your code from the command line by typing
     python3 scriptname.py
   • Running code in interactive Jupyter notebook from the university environment:
     conda install -c conda-forge jupyterlab
     After installing that once, the notebook/lab can be started using:
     jupyter lab
     (When using Jupyter through SSH, so when it is not running on the machine that you are currently working on, make sure to use port forwarding whilst SSH-ing, using options -g -L 8888:127.0.0.1:8888. Also add options --no-browser and --port=8888 when starting up Jupyter.)

Instructions for today: Lab session on NetworkX

1. Take some time to do the NetworkX tutorial.
2. Have a look at functionality to read and write graphs from/to disk, and in particular learn how to import an edge list. Understand the input format and ways of including things like Weight and other attributes of edges.
3. A lot of the common network metrics you may want to compute are implemented as NetworkX function or NetworkX algorithm. Become familiar with these, for example by computing measures such as degree assortativity, clustering, density, diameter and average distance.
4. While at it, why not take a look at how NetworkX relates to other data formats?
5. Try to load the network from the first lab session (small-gephiready.tsv) into NetworkX, and investigate some characteristic properties of the network, such as the degree distribution and distance distribution. Use theread_edgelist function and remember to select the correct separator (tab), and pay attention to the header of the file.
6. Get the Epinions network from the SNAP repository. You may need to fiddle with the precise header, but it is already in edge list format. Compute some common characteristics such as the degree distribution, and visualize them using appropriate figures or distributions, for example using pyplot/Matplotlib.
7. What are the differences between NetworkX and Gephi in terms of visualization and analysis capabilities?

Done? Proceed with Exercise 2 of Assignment 1.

Looking for a challenge? Check out these three alternatives (that you can also use instead of NetworkX throughout the course, if you prefer (but for which there is less help available)):

• Python igraph, a Python version of the R igraph package.
• Graph-Tool, a python graph analysis toolkit that leaves the hard computation to parallel (OpenMP) C++ code.
• SNAP, which is entirely written in C++ and has many interesting features. Nowadays, there is also a Python version.

Running intro problems regarding prior knowledge on python programming? See the reading material at the bottom of this course website.

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, 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 topics such as centrality and communities, which will not be covered extensively until Lecture 2, 3 and 4.

Done? Get started with the practical part of Assignment 1. You can download the datafiles here. 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.