{"id":27886500,"url":"https://github.com/stellargraph/stellar-practical-ml-on-graphs","last_synced_at":"2025-05-05T08:02:33.119Z","repository":{"id":56333240,"uuid":"168428492","full_name":"stellargraph/stellar-practical-ml-on-graphs","owner":"stellargraph","description":"Course titled Practical Machine Learning on Graphs","archived":false,"fork":false,"pushed_at":"2020-11-13T18:42:02.000Z","size":61635,"stargazers_count":41,"open_issues_count":3,"forks_count":15,"subscribers_count":16,"default_branch":"master","last_synced_at":"2023-03-06T12:51:58.949Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://www.stellargraph.io/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/stellargraph.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-01-30T22:58:30.000Z","updated_at":"2023-02-13T23:31:21.000Z","dependencies_parsed_at":"2022-08-15T16:50:31.668Z","dependency_job_id":null,"html_url":"https://github.com/stellargraph/stellar-practical-ml-on-graphs","commit_stats":null,"previous_names":[],"tags_count":null,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stellargraph%2Fstellar-practical-ml-on-graphs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stellargraph%2Fstellar-practical-ml-on-graphs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stellargraph%2Fstellar-practical-ml-on-graphs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stellargraph%2Fstellar-practical-ml-on-graphs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/stellargraph","download_url":"https://codeload.github.com/stellargraph/stellar-practical-ml-on-graphs/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252463295,"owners_count":21751759,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-05-05T08:00:42.195Z","updated_at":"2025-05-05T08:02:33.113Z","avatar_url":"https://github.com/stellargraph.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Practical Machine Learning on Graphs course\n\n## Overview\n\nThis course introduces graph machine learning, with a practical focus based on the [StellarGraph library](https://github.com/stellargraph/stellargraph).\n\nThe course slides are available in [the `slides/` directory](slides).\n\n## Preparation\n\nThe main requirements are **Python 3.6 or 3.7** and a set of python modules listed in the file **requirements.txt** \nwith the correct module version numbers. \n\nWe provide installation instructions for Windows 10 and MacOS. The latter can be used as a guide for common Linux \ndistributions such as Ubuntu. We expect that users have some experience programming in Python and using basic tools \nsuch as pip and git.\n\n### Instructions for MacOS\n\n[1] Install a Python 3 (version 3.6 or 3.7) distribution. We recommend \nAnaconda that can be downloaded by clicking\n[here.](https://conda.io/docs/user-guide/install/download.html) Alternatively, install\nPython 3.6 from [here](https://www.python.org/downloads/release/python-369/) and also\n`virtualenv` via the command `pip install virtualenv`.\n\n[2] Download and install the latest version of `git` from [here.](https://git-scm.com/download/mac)\n\n[3] Create a working directory, e.g., `/Users/YOUR-USER-NAME/Projects`, where `YOUR_USER_NAME`\nshould be replaced with your user name and `Projects` is the new working directory. From this\npoint on we will use `HOME` to refer to `/Users/YOUR-USER_NAME` such that the working\ndirectory will be `HOME/Projects`\n\n[4] Create a custom environment using the command (if using conda)\n\n `conda create --name practical-ml python=3.6` \n \n Alternatively, if you are not using Anaconda and conda, you can create a new virtual \n environment using `virtualenv`. Instructions on how to install and use `virtualenv` can be \n found [here.](https://virtualenv.pypa.io/en/stable/)\n \n `virtualenv practical-ml`\n \n The above command will create a new Python environment at location `HOME/Projects/practical-ml`\n\n[5] Create the file `matplotlibrc` in directory `~/.matplotlib`; create the directory if it does\nnot exist in your system. The file contents should be,\n\n`backend:TkAgg`\n \n[6] Activate the new virtual environment created with `conda` by executing the command,\n\n`source activate practical-ml`\n\nor if created using `virtualenv` use the command,\n\n`source practical-ml/bin/activate`\n\n[7] Download or clone the course source code repository  using the command (from the `HOME/Projects` directory),\n\n`git clone https://github.com/stellargraph/stellar-practical-ml-on-graphs.git`\n\n[8] Change to the `stellar-practical-ml-on-graphs` directory and install the python requirements using the command,\n\n`pip install -r requirements.txt`\n\n[9] You also need to register the `practical-ml` environment so that it is available in `Jupyter`. You \nshould use the following command,\n\n`python -m ipykernel install --user --name=practical-ml`\n\n[10] You can verify that you have the correct version of stellargraph installed by using the command,\n\n `python -c \"import stellargraph as sg; print(sg.__version__)\"`\n\nPay attention to the double quotes and the __ is two underscores (before and after the word version.) The above command \nshould print\n\n`0.10.0`\n\n[11] You can now run jupyter notebook using the command,\n\n`jupyter notebook`\n\nYou can access the course notebooks using your web browser at localhost:8888\n\n\n**The installation should now be complete.**\n\n\n### Instructions for Windows 10\n\n[1] Install a Python 3 (version 3.6 or 3.7) distribution. You can download the recommended \nversion from [here](https://www.python.org/downloads/release/python-362/). Make sure to select the\noption to **Add Python 3.6 to PATH** on the Setup screen.\n\n\n[2] Open a Windows Command Prompt and run the following command,\n\n`python --version`\n\nif the installation was successful then it should print,\n\n`Python 3.6.2`\n\nThe default Python distribution also includes `pip`. On the Command Prompt type the following\ncommand,\n\n`pip --version`\n\nif the installation was successful then it should print the pip version,\n\n`pip 19.0.2`\n\n[3] Install `virtualenv` using the following command,\n\n`pip install virtualenv`\n\n[4] Install `Jupyter Notebook` using the command,\n\n`pip install jupyter`\n\n[5] Download and install the latest version of `git` from [here.](https://git-scm.com/download/win)\n\n[6] Create a working directory, e.g., `C:\\users\\YOUR-USER-NAME\\Projects`, where `YOUR_USER_NAME`\nshould be replaced with your user name and `Projects` is the new working directory. From this\npoint on we will use `HOME` to refer to `C:\\users\\YOUR-USER_NAME` such that the working\ndirectory will be `HOME\\Projects`\n\n[7] Change to the `HOME\\Projects` directory and create a new Python virtual environment called `practical-ml`\nusing the following command,\n\n`virtualenv practical-ml`\n\nThis will create a new folder `HOME\\Projects\\practical-ml`\n\n[8] Activate the new environment using the command,\n\n`practical-ml\\Scripts\\activate`\n\n[9] Download or clone the `stellar-practical-ml-on-graphs` repository (in `HOME\\Projects`) using the \ncommand,\n\n`git clone https://github.com/stellargraph/stellar-practical-ml-on-graphs.git`\n\n[10] Change to the `stellar-practical-ml-on-graphs` directory and install the python requirements using\nthe command,\n\n`pip install -r requirements.txt`\n\n[12] You also need to register the `practical-ml` environment so that it is available in `Jupyter`. \nYou should use the following command,\n\n`python -m ipykernel install --user --name=practical-ml`\n\n[13] You can verify that you have the correct version of `stellargraph` installed by using the command,\n \n`python -c “import stellargraph as sg; print(sg.__version__)”`\n \nPay attention to the double quotes and the __ is two underscores (before and after the word version.) The above command should print\n\n`0.10.0`\n\n[14] You can now run jupyter notebook using the command,\n\n`jupyter notebook`\n\nYou can access the course notebooks using your web browser at localhost:8888\n\n\n**The installation should now be complete.**\n\n## License\n\nCopyright 2010-2020 Commonwealth Scientific and Industrial Research Organisation (CSIRO).\n\nAll Rights Reserved.\n\nNOTICE: All information contained herein remains the property of the CSIRO. The intellectual and technical concepts\ncontained herein are proprietary to the CSIRO and are protected by copyright law. Dissemination of this information \nor reproduction of this material is strictly forbidden unless prior written permission is obtained from the CSIRO.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstellargraph%2Fstellar-practical-ml-on-graphs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstellargraph%2Fstellar-practical-ml-on-graphs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstellargraph%2Fstellar-practical-ml-on-graphs/lists"}