Social Network Analysis for Computer Scientists

Code review (week 12). The main goal of this code review session is to is conduct a review of another team's code and experimental setup, and to receive feedback on one's own code. The mandatory deliverable for today is a contribution to the SNACS Collaborative Code Review 2021 Best Practices document.
What to do? Watch the code review lecture movie, and follow the instructions therein. You can also have a look at the code review session slides.

We use the same division into teams as for the peer review in week 10. In case team pairs are not complete, contact the course staff around Snellius 302/304. Students who are not able to come to the university because of the campus protocol and/or other corona-related reasons, can join the online session (see Brigthspace for Zoom URL), where pairing will be done on the spot.

Peer review (week 10). The main goal of this peer review session is to conduct a review of another team's draft project paper, and to receive feedback on one's own paper. The mandatory deliverable for today is a short report on the feedback you were able to give the other team, to be handed in via Brightspace at the end of the session.
What to do? Watch the peer review lecture movie, and follow the instructions therein. You can also have a look at the peer review session slides.

Team matching

Please find the team you are matched with, and ask help from the assistants if needed. Each line lists two teams that are matched up.

1. Anomaly detection 1	2. Anomaly detection 2
3. Anonymity in networks	41. Anonymity in networks
4. Betweenness centrality 1	10. Community detection 3
6. Closeness centrality 1	7. Closeness centrality 2 (top-k)
8. Community detection 1	9. Community detection 2
11. Community detection 4	44. Community detection 4
12. Data errors in networks 1	13. Data errors in networks 2
14. Dense subgraph detection	42. Dense subgraph detection
15. Diameter computation	17. Graph evolution rules
16. Graph compression	45. Graph compression
18. Graph obfuscation	46. Graph obfuscation
19. Influence spread and virality 1	20. Influence spread and virality 2
21. Link prediction 1	23. Link prediction in signed networks
22. Link prediction 2	52. Link prediction 2
24. Link prediction in temporal networks	51. Shortest paths 2
25. Local cluster detection	47. Local cluster detection
26. Motifs in multilayer networks	27. Motifs in networks
28. Motifs in temporal networks	29. Neighborhood approximation
30. Network embeddings 1	31. Network embeddings 2
32. Personalized PageRank	49. Personalized PageRank
33. Sampling from networks 1	34. Sampling from networks 2
35. Shortest paths 1	50. Shortest paths 1
37. Triangle counting	39. Visualization algorithms 2
38. Visualization algorithms 1	40. Visualization algorithms 3

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 started with both the course project and with Assignment 2, and 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. 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.

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 200 words what you plan to do for your course project, and feel free to discuss this with the lecturer or an assistant for feedback. Also see the generic instructions for the Course Project.

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

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).
   Download Gephi 0.9.2 for Windows, Linux or Mac (mirrors provided because of occasional slow speed of gephi.org downloads).
2. NetworkX, a more extensive Python package for network analysis that can handle larger network datasets and computations (next week).
   Other even faster tools such as SNAP (in C++) as well as igraph and graph-tool, can also be used, but require some more figuring out by the student.

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,
• And export a vector graphic PDF of your network,
• 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.
   Java should be installed on all machines; Gephi likely as well.
2. On the Windows machines in the Snellius computer rooms, install Gephi in D:\ (ignore registry key warnings) and edit the gephi.conf file in the Gephi etc folder to state
   jdkhome="C:\Program Files\IBM\SPSS\Statistics\25\JRE"
   Then run gephi.exe
3. If Gephi does not immediately work on your marchine, you may have to adjust one line in etc/gephi.conf so that Gephi can find the right Java version (and remove the # in front of that line), reading something like jdkhome="/usr/lib/jvm/java-8-openjdk-amd64/" (or the equivalent Windows path). Also, Windows users sometimes have to launch the gephi64.exe instead of gephi.exe (or the other way around, depending on Java and OS version(s)).
4. Walk through the Gephi tutorials. The Gephi website has various official tutorials that you can walk through; in particular Quick start, Visualization and Layouts. 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.
   
   
5. 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.
   
   
6. 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).
   
   
7. 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.
   
   
8. 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.

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 work on a machine in the student computer rooms.
   
   • ISSC infrastructure. First, connect to the gateway sshgw.leidenuniv.nl. Then, navigate to a student computer (see the REL page on SSH access, only accessible internally, for a list of computers), or hop on to the remote server remotelx.liacs.leidenuniv.nl.

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_edgelistfunction 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 heading, 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 two 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.