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@@ -29,9 +29,9 @@ It supports a wide variety of structures like low-order structures (graph, direc
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In addition, DHG's [dhg.experiments](https://deephypergraph.readthedocs.io/en/latest/api/experiments.html) module (that implements **Auto-ML** upon [Optuna](https://optuna.org)) can help you automatically tune the hyper-parameters of your models in training and easily outperforms the state-of-the-art models.
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*[Hightlights](#highlights)
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*[Installation](#installation)
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You can draw the graph, hypergraph, directed graph, and bipartite graph with DHG's visualization tool. More details see the [Tutorial](https://deephypergraph.readthedocs.io/en/latest/tutorial/vis_structure.html)
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```python
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import matplotlib.pyplot as plt
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plt.show()
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```
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```python
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import matplotlib.pyplot as plt
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-**[News20](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.News20.html#dhg.data.News20)**: A newspaper network dataset for vertex classification task.
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-**[DBLP8k](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.DBLP8k.html#dhg.data.DBLP8k)**: The DBLP-8k dataset is a citation network dataset for link prediction task.
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