Social Network Analysis for Computer Scientists

The main goal of this lab session is to ensure that your team is all set for making serious progress writing the course project paper in the coming weeks.

Course project paper
Earlier, you have made a project planning. One upcoming deadline is the first version of your paper for the peer review session (November 11).

1. Template. If you have not yet done so, download the paper template and make sure you can compile it and understand how to use it. Remember to fill in the meta information.
2. Related work. If you have not yet done so, spend some time at Google Scholar investigating a) what other papers exist on this topic, b) which relevant papers cite your paper, and c) what important references are presented in your paper. You will likely want to include some of these in your paper as well. At least make sure that you are aware of the major works that cite your paper, and that you reference some of these works in your introduction when you sketch the context of your work.
3. Project contribution. For the course project, you have to do something original. Ideally, this goes beyond the one paper that you were assigned, comparing techniques from multiple papers, for example comparing different algorithms or methods, using different validation metrics, or testing on (a) larger (number of diverse) datasets. Try to get this written down in the appropriate section(s) of your paper.
4. Collaboration. How will you work together on the paper? Git? Overleaf? How will you share code, data and results with each other, and ensure integration of it all?
5. What is a good paper?. What defines a good paper? Discuss with your project partner to align your expections. When are you "done"? When is the paper "sufficient"? When is it "good"? "Excellent"?
6. Writing. Make serious progress with writing the first 3 to 4 sections of your course project paper in the coming two weeks, so that you have something substantial for the peer review session.
7. Planning. Look at the planning you made early October. Have you reserved time to review each other's texts? Are adjustments necessary?

As always: please ask course staff for help if you are still unsure about certain aspects of your project.

The main goal of this lab session is to get to know the data science lab.

If you are working remotely, learn how-to set up remote access to the LIACS Research and Education Laboratory (REL)

Data Science Lab
The data science lab website provides necessary information and documentation. The emailadres to contact in case of access issues or other technical problems is rel@liacs.leidenuniv.nl.

Become familiar with the lab and how to run code on it, and how to place data within the lab. This may be handy for Assignment 2 and/or the course project. Remember:

• /home/sXXXXXX, your homedirectory ~, is for your own code (don't put large stuff there),
• /local is for local storage on the current machine you are are on,
• /data is for storing data across data science lab machines.

Think of setting up passwordless login, consider using ProxyJump to avoid the gateway, and perhaps use sshfs to mount your remote homedirectory. Some IDEs also offer all of this functionality.

Assignment 2
Continue with the practical part of Assignment 2.

The main goal of this lab session is to get started with both the course project and with Assignment 2.

Course project
Below are some topics you can discuss and investigate together with your project team partner.

1. Project schedule. Have a look at the deadlines for the course project in the schedule on this website, and create a sensible planning with your team partner.
2. Paper. Read your course project paper, and spend some time at Google Scholar investigating a) what other papers exist on this topic, b) which relevant papers cite your paper, and c) what important references are presented in your paper.
3. Data repositories. Check out the data repositories of real (social) networks such as SNAP and KONECT and think of datasets suitable for your course project.
4. Project contribution. For the course project, you have to do something original. Ideally, this goes beyond the one paper that you were assigned, comparing techniques from multiple papers, for example comparing different algorithms or methods, using different validation metrics, or testing on (a) larger (number of diverse) datasets. Write down in at most 150 words what you plan to do for your course project, and feel free to discuss this with the lecturer or an assistant for feedback, this week or the next. Also see the generic instructions for the Course Project.

Done? Get started with the practical part of Assignment 2.

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.
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).
2. Alternatively, you can use the desktop machines in the student computer rooms (proceed to the next step),
   or the university's remote SSH functionality to remotely work on these machines via the gateway sshgw.leidenuniv.nl, and then connecting to a student computer (see the REL page on SSH access, only accessible internally, for a list of computer names).

Installing and using conda. To create a Python environment on the university computers or servers, it is easiest to use conda.

1. Download Miniconda by running the following command in your terminal: wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
2. Install Miniconda: bash Miniconda3-latest-Linux-x86_64.sh
   The installer will ask you some questions. Normally, the standard location suffices and you do not have to add conda to path.
3. After installation finishes, close your terminal so that the changes take effect. Upon opening a new terminal, activate the conda virtual environment with: source miniconda3/bin/activate
   If all goes well, you will see that (base) appears in front of your shell prompt. This means that conda's base environment is active.
4. Next, 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 will prevent this issue.
   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

Running code from the command line. Now you should be able to run your code from the command line by typing
python3 scriptname.py
Bonus: If you want to use an interactive Jupyter notebook instead, this is possible with the following commands:
conda install -c conda-forge jupyterlab
jupyter-notebook
When using Jupyter through SSH, 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.

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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 of distributions, for example using 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)):

• 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.
• Python igraph, a Python version of the R igraph package.

About social network analysis tools and packages. There exist different tools and package for social network analysis. In this course, we will introduce you to 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).
   Next week (week 3) we get started with NetworkX.

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.

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Instructions for today: Lab session on Gephi.
The following steps and tutorials will help you get to know Gephi.

1. Install and run Gephi locally on your computer. Installation instructions, if needed, can be found on the Gephi website.
   You will need Java JRE installed. (If you use the Windows machines in the Snellius computer rooms, install Gephi in D:\ and ignore registry key warnings). An older version may be preinstalled in Linux.
2. Walk through the Gephi tutorials. The Gephi website has various official tutorials that you can walk through; in particular Quick start, Visualization and Layouts. However, you may benefit from using a more modern tutorial (courtesy of Derek Geene at UCD).
   Note: One reason to use a more modern tutorial is that the official Gephi Quick Start Guide is for a slightly older version of Gephi. On p. 9-10, contrary to what is indicated in the getting started guide, the ranking module is part of the appearance module (since Gephi 0.9), and does not appear as a separate tab. The “Ranking result” table no longer exists, and you can skip that part of the tutorial. In the tutorial on Visualization, on p. 9 the 3-D visualisation option was bugged and has been removed in Gephi 0.9. You can skip that part of the tutorial.
   
   
3. Compute relevant real-world network dataset properties. Try out some of the Gephi datasets and see if you can understand how they differ in terms of density, degree distribution, giant components, clustering and average distance.
   
   
4. Walk through a tutorial on data import and export. Learn how to import raw data from this short data import tutorial. Visualize the network represented by the edge list small-gephiready.tsv. Compute the degree for each node and export it to a node list file (which you can for example reuse in other plotting tools).
   
   
5. Visualization and exporting as vector graphic. Play around with some different visualization algorithms such as ForceAtlas 2 (for example, with scaling set to a higher value and with stronger gravity checked) and Fruchterman Reingold. Try to visualize the network such that labels remain readable.
   Hints: From a random initialization, use the "ForceAtlas 2" visualization algorithm perhaps with "Stronger gravity" and usually with "Scaling" set to a much higher than default value. Increase the overall node size if needed. Once stable, you can improve the layout by enabling parameter "Prevent overlap". Run "Expansion" to make some more space between the nodes, then enable the display of node labels, properly size them, and finally run "Label Adjust" to prevent label overlap.
   Export your visualization to PDF so that you have it as a vector graphic.
   
   
6. Create, import and export your own social network data. Make a new, very simple, edge list input file of a fictive network with say 10 nodes and 20 edges, and import it into Gephi. Now add to the edge input file (in some order) link (un)direction, link weights and link labels.
   Also import a node list file with node labels and other properties. Be sure to append it to your existing workspace and to use identifiers.
   For example, have nodes represent students, and link represent friendships, and choose node and edge labels accordingly. Make sure that you are able understand the difference in file input and visualization output, and that you are comfortable with importing and exporting data into and from Gephi.
   
   

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.