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    <h1>PyGCL</h1>
    <p>PyGCL is a PyTorch-based open-source Graph Contrastive Learning (GCL)
      library, which features modularized GCL components from published papers,
      standardized evaluation, and experiment management. </p>
    <p><a class="btn btn-primary btn-lg"
        href="{{ "/docs/home/" | relative_url }}" role="button">Quickstart</a>
    <a class="btn btn-warning btn-lg"
      href="https://pygcl.readthedocs.io/" role="button">Read Docs</a>
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    <h1 class="text-center">Installation</h1>
    <p>To install PyGCL with <code>pip</code>, simply run:</p>
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        <pre class="highlight"><code>pip <span class="nb">install </span>PyGCL</code></pre>
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    <p>Then, you can import <code>GCL</code> from your current environment.</p>
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    <h3>Prerequisites</h3>
    <ul>
      <li>Python 3.8+</li>
      <li>PyTorch 1.9+</li>
      <li>PyTorch-Geometric 2.0+</li>
      <li>DGL 0.7+</li>
      <li>Scikit-learn 0.24+</li>
      <li>Numpy</li>
      <li>tqdm</li>
      <li>NetworkX</li>
    </ul>
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      <h3 class="text-center">Modularized Design</h3>
      <p>PyGCL features modularized design with four critical components of graph contrastive learning,
        which can be independently used or integrated into existing implementations.
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      <h3 class="text-center">Open Source</h3>
      <p>PyGCL is licensed under the <a href="https://github.com/PyGCL/PyGCL/blob/develop/LICENSE">Apache License 2.0</a>
        and actively maintained by the community on <a href="https://github.com/PyGCL/PyGCL">GitHub</a>.</p>
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      <h3 class="text-center">Wide Coverage</h3>
      <p>PyGCL covers a wide range of up-to-date models that are representative in open literature.
        It can also be easily extended to include more design choices.
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