https://github.com/mdeff/learning-from-graphs-webconf2021

Learning from Graphs: From Mathematical Principles to Practical Tools
https://github.com/mdeff/learning-from-graphs-webconf2021

Last synced: 8 months ago
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Learning from Graphs: From Mathematical Principles to Practical Tools

• Host: GitHub
• URL: https://github.com/mdeff/learning-from-graphs-webconf2021
• Owner: mdeff
• License: mit
• Created: 2021-02-24T02:25:37.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2021-04-16T01:22:50.000Z (about 5 years ago)
• Last Synced: 2025-03-16T01:13:21.847Z (over 1 year ago)
• Language: Makefile
• Size: 168 KB
• Stars: 11
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

          
# Learning from Graphs: From Mathematical Principles to Practical Tools

A hands-on tutorial presented by [Michaël Defferrard](https://deff.ch) at [The Web Conference 2021](https://www2021.thewebconf.org/program/tutorials).

> A graph encodes relations between objects, such as distances between points or hyperlinks between websites.

> You will learn how to extract information about that relational structure.

> This information is crucial to characterize an object through its local connectivity or an entire graph through its global connectivity.

> On top of that structure, a network may possess data about the objects or the relations, such as a point's color or an hyperlink's click-through rate.

> You will learn how to leverage a graph to analyze this data.

> Leveraging the structure that underlies data is an important concept, from physical symmetries dictating conservation laws to the efficiency of convolutional neural networks.

> The tutorial is built on deep mathematical principles but will walk you from the basics with an emphasis on intuitions and working knowledge.

**Intended audience**: people with theoretical or practical interests about graphs and data on graphs. Adapted to both researchers and practitioners.

**Prerequisite knowledge**: scientific python programming, basic linear algebra, no prior knowledge about networks is necessary.

## Installation

[![Binder](https://mybinder.org/badge_logo.svg)][binder]

  Click the binder badge to run code from your browser without installing anything.

[binder]: https://mybinder.org/v2/gh/mdeff/learning-from-graphs-webconf2021/HEAD?urlpath=lab

For a local installation, you will need [git], [Python], [Jupyter], and packages from the [Python scientific stack][scipy].

If you don't know how to install those on your platform, we recommend to install [Miniconda] or [Anaconda], a distribution of the [conda] package and environment manager.

Follow the below instructions to install it and create an environment for the course.

1. Download the Python 3.x installer for Windows, macOS, or Linux from  and install with default settings.

   Skip this step if you have conda already installed (from [Miniconda] or [Anaconda]).

   * Windows: double-click on `Miniconda3-latest-Windows-x86_64.exe`.

   * macOS: double-click on `Miniconda3-latest-MacOSX-x86_64.pkg` or run `bash Miniconda3-latest-MacOSX-x86_64.sh` in a terminal.

   * Linux: run `bash Miniconda3-latest-Linux-x86_64.sh` in a terminal or use your package manager.

1. Open a terminal.

   Windows: open the Anaconda Prompt from the Start menu.

1. Install git with `conda install git`.

1. Navigate to the folder where you want to store the material with `cd path/to/folder`.

1. Download this repository with `git clone https://github.com/mdeff/learning-from-graphs-webconf2021`.

1. Enter the repository with `cd learning-from-graphs-webconf2021`.

1. Create an environment containing the required packages with `conda env create -f environment.yml`.

Every time you want to work, do the following:

1. Open a terminal.

   Windows: open the Anaconda Prompt from the Start menu.

1. Activate the environment with `conda activate learning-from-graphs-webconf2021`.

1. Navigate to the folder where the material is stored with `cd path/to/folder/learning-from-graphs-webconf2021`.

1. Start Jupyter with `jupyter lab`.

   The command should open a new tab in your web browser.

1. Edit and run the notebooks from your browser.

1. Once done, you can run `conda deactivate` to leave the environment.

[git]: https://git-scm.com

[python]: https://www.python.org

[jupyter]: https://jupyter.org

[scipy]: https://www.scipy.org

[anaconda]: https://www.anaconda.com/products/individual

[miniconda]: https://conda.io/miniconda.html

[conda]: https://conda.io

[conda-forge]: https://conda-forge.org

## License

The content is released under the terms of the [MIT License](LICENSE.txt).