This application provides a collection of graph datasets suitable for machine learning projects, including Graph Neural Networks. Graph datasets are essential for working on tasks such as node classification, link prediction, and graph classification, among others.
To use the graph-datasets application, your computer should meet the following requirements:
- Operating System: Works on Windows, macOS, and Linux.
- Memory (RAM): At least 4 GB is recommended for smooth operation.
- Disk Space: Minimum of 100 MB of free space is required to download and install the datasets successfully.
To download the latest version of the graph-datasets application, follow these steps:
- Visit this page to download: Graph Datasets Releases.
- You will see a list of available releases. Find the latest version listed at the top.
- Click on the version to view its details and downloadable files.
- Select the appropriate file for your operating system:
- For Windows, look for files ending in
.zipor.exe. - For macOS, find files ending in
.tar.gz. - For Linux, choose files that suit your distribution, commonly
.tar.gzor.deb.
- For Windows, look for files ending in
- Download the selected file to your computer.
- Once downloaded, extract the files if necessary and follow the included instructions to run the application.
You can also visit this page again to check for updates and new datasets: Graph Datasets Releases.
The graph-datasets application includes a variety of datasets. Here are some examples:
- Description: Data representing social interactions in various platforms. Use this data for tasks such as community detection and social influence analysis.
- Description: Data on academic papers and their citations. This dataset helps in tasks like recommendation systems and academic influence analysis.
- Description: Includes graphs representing geographic data, ideal for projects focused on spatial analysis and routing problems.
Each dataset is categorized and includes information about the number of nodes, edges, and any features that may be included. Make sure to explore the dataset documentation included in the downloaded files for specific details.
After downloading the datasets, you will find a README file in the extracted folder. Follow these steps to use the datasets:
- Open the README file. It contains detailed instructions on how to load the datasets into your project.
- Use the dataset by following the provided examples. You can load the datasets using common Python libraries like NetworkX or PyTorch Geometric.
- Explore the dataset and visualize it using tools like Matplotlib or Gephi.
If you encounter any issues or have questions, feel free to check the Issues page on GitHub. The community is here to help you.
If you want to contribute by adding new datasets or improving existing ones, please follow these steps:
- Fork the repository.
- Create a new branch.
- Make your changes and commit them.
- Push your branch and create a pull request.
- Our maintainers will review your contributions and merge them if they meet the guidelines.
We appreciate your interest in improving the graph-datasets project.
Each dataset comes with documentation. Review it to find the right fit for your needs.
Yes, the datasets are open-source. However, please check the licensing in each dataset's documentation for details on usage rights.
We welcome suggestions for new datasets. Feel free to open a new issue on GitHub to request datasets.
For further inquiries or support, you can reach out via the GitHub platform or contact the project maintainers directly through their profiles.
To download the latest updates and explore the new datasets, remember to visit: Graph Datasets Releases.