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        <h1>Temporal Graph Benchmark</h1>
        <p>Benchmark datasets for machine learning on temporal graphs, temporal knowledge graphs and temporal heterogeneous graphs.</p>
        <p><a class="btn btn-primary btn-lg" href="{{ "/docs/home/" | relative_url }}" role="button">Get Started</a></p>
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            <h2 class="header-light regular-pad">Temporal Graph Benchmark</h2>
              <h6 class="lead">TGB is a collection of challenging and diverse benchmark datasets 
                for realistic, reproducible, and robust evaluation for machine learning on temporal graphs.
                TGB includes both dynamic link and node property prediction tasks and an automated pipeline 
                from dataset downloading, dataloading, evaluation and submission to the TGB leaderboard. 
                In TGB 2.0, we also included novel datasets for temporal knowledge graphs and temporal heterogeneous graphs.
                TGB is driven by community feedback and suggestions, if you would like to contribute a novel dataset or report any issues, 
                please <a href="shenyang.huang@mail.mcgill.ca">email us</a> or visit our <a href="https://github.com/shenyangHuang/TGB">github</a>.</h6>
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            <h3 class="text-center">Diverse Datasets</h3>
            <h5>TGB provides diverse and realistic datasets, containing millions of nodes, edges and timestamps</h5>
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            <h3 class="text-center">Multiple Data Formats</h3>
            <h5>TGB datasets are supported as <code>numpy</code> arrays,  <code>PyTorch</code> tensors and <code>PyG</code> compatible <code>TemporalData</code> objects</h5>
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            <h3 class="text-center">Unified Evaluation</h3>
            <h5>TGB provides dataset splits and evaluators for reproducible and standardized evaluation for temporal graphs, temporal knowledge graphs and temporal heterogeneous graphs.</h5>
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