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<img src="https://deephypergraph.com/logo_DHG.png" height="200">
</p>
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**[Website](https://deephypergraph.com/)** | **[Documentation](https://deephypergraph.readthedocs.io/)** | **[Tutorials](https://deephypergraph.readthedocs.io/en/latest/tutorial/overview.html)** | **[中文文档](https://deephypergraph.readthedocs.io/en/latest/zh/overview.html)** | **[Official Examples](https://deephypergraph.readthedocs.io/en/latest/examples/vertex_cls/index.html)** | **[Discussions](https://github.com/iMoonLab/DeepHypergraph/discussions)**
## News
- 2022-12-28 -> **v0.9.3** is now available! More datasets and operations of hypergraph are included!
- 2022-12-28 -> **v0.9.3** 正式发布！ 包含更多数据集和超图操作！
- 2022-09-25 -> **v0.9.2** is now available! More datasets, SOTA models, and visualizations are included!
- 2022-09-25 -> **v0.9.2** 正式发布！ 包含更多数据集、最新模型和可视化功能！
- 2022-08-25 -> DHG's first version **v0.9.1** is now available!
- 2022-08-25 -> DHG的第一个版本 **v0.9.1** 正式发布！
**DHG** *(DeepHypergraph)* is a deep learning library built upon [PyTorch](https://pytorch.org) for learning with both Graph Neural Networks and Hypergraph Neural Networks. It is a general framework that supports both low-order and high-order message passing like **from vertex to vertex**, **from vertex in one domain to vertex in another domain**, **from vertex to hyperedge**, **from hyperedge to vertex**, **from vertex set to vertex set**.
It supports a wide variety of structures like low-order structures (graph, directed graph, bipartite graph, etc.), high-order structures (hypergraph, etc.). Various spectral-based operations (like Laplacian-based smoothing) and spatial-based operations (like message psssing from domain to domain) are integrated inside different structures. It provides multiple common metrics for performance evaluation on different tasks. Many state-of-the-art models are implemented and can be easily used for research. We also provide various visualization tools for both low-order structures and high-order structures.
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.
* [Hightlights](#highlights)
* [Installation](#installation)
* [Quick Start](#quick-start)
* [Examples](#examples)
* [Datasets](#datasets)
* [Metrics](#metrics)
* [Implemented Models](#implemented-models)
---------------------------------------------------------------
## Highlights
- **Support High-Order Message Passing on Structure**:
DHG supports pair-wise message passing on the graph structure and beyond-pair-wise message passing on the hypergraph structure.
- **Shared Ecosystem with Pytorch Framework**:
DHG is built upon Pytorch, and any Pytorch-based models can be integrated into DHG. If you are familiar with Pytorch, you can easily use DHG.
- **Powerful API for Designing GNNs and HGNNs**:
DHG provides various Laplacian matrices and message passing functions to help build your spectral/spatial-based models, respectively.
- **Visualization of Graphs and Hypergraphs**
DHG provides a powerful visualization tool for graph and hypergraph. You can easily visualize the structure of your graph and hypergraph.
- **Bridge the Gap between Graphs and Hypergraphs**:
DHG provides functions to build hypergraph from graph and build graph from hypergraph. Maybe promoting the graph to hypergraph can exploit those potential high-order connections and improve the performance of your model.
- **Attach Spectral/Spatial-Based Operations to Structure**:
In DHG, those Laplacian matrices and message passing functions are attached to the graph/hypergraph structure. As soon as you build a structure with DHG, those functions will be ready to be used in the process of building your model.
- **Comprehensive, Flexible, and Convenience**:
DHG provides random graph/hypergraph generators, various state-of-the-art graph/hypergraph convolutional layers and models, various public graph/hypergraph datasets, and various evaluation metrics.
- **Support Tuning Structure and Model with Auto-ML**:
The Optuna library endows DHG with the Auto-ML ability. DHG supports automatically searching the optimal configurations for the construction of graph/hypergraph structure and the optimal hyper-parameters for your model and training.
## Installation
Current, the stable version of **DHG** is 0.9.3. You can install it with ``pip`` as follows:
```python
pip install dhg
```
You can also try the nightly version (0.9.4) of **DHG** library with ``pip`` as follows:
```python
pip install git+https://github.com/iMoonLab/DeepHypergraph.git
```
Nightly version is the development version of **DHG**. It may include the lastest SOTA methods and datasets, but it can also be unstable and not fully tested.
If you find any bugs, please report it to us in [GitHub Issues](https://github.com/iMoonLab/DeepHypergraph/issues).
## Quick Start
### Visualization
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)
```python
import matplotlib.pyplot as plt
import dhg
# draw a graph
g = dhg.random.graph_Gnm(10, 12)
g.draw()
# draw a hypergraph
hg = dhg.random.hypergraph_Gnm(10, 8)
hg.draw()
# show figures
plt.show()
```
```python
import matplotlib.pyplot as plt
import dhg
# draw a directed graph
g = dhg.random.digraph_Gnm(12, 18)
g.draw()
# draw a bipartite graph
g = dhg.random.bigraph_Gnm(30, 40, 20)
g.draw()
# show figures
plt.show()
```
### Learning on Low-Order Structures
On graph structures, you can smooth a given vertex features with GCN's Laplacian matrix by:
```python
import torch
import dhg
g = dhg.random.graph_Gnm(5, 8)
X = torch.rand(5, 2)
X_ = g.smoothing_with_GCN(X)
```
On graph structures, you can pass messages from vertex to vertex with `mean` aggregation by:
```python
import torch
import dhg
g = dhg.random.graph_Gnm(5, 8)
X = torch.rand(5, 2)
X_ = g.v2v(X, aggr="mean")
```
On directed graph structures, you can pass messages from vertex to vertex with `mean` aggregation by:
```python
import torch
import dhg
g = dhg.random.digraph_Gnm(5, 8)
X = torch.rand(5, 2)
X_ = g.v2v(X, aggr="mean")
```
On bipartite graph structures, you can smoothing vertex features with GCN's Laplacian matrix by:
```python
import torch
import dhg
g = dhg.random.bigraph_Gnm(3, 5, 8)
X_u, X_v = torch.rand(3, 2), torch.rand(5, 2)
X = torch.cat([X_u, X_v], dim=0)
X_ = g.smoothing_with_GCN(X, aggr="mean")
```
On bipartite graph structures, you can pass messages from vertex in `U` set to vertex in `V` set by `mean` aggregation by:
```python
import torch
import dhg
g = dhg.random.bigraph_Gnm(3, 5, 8)
X_u, X_v = torch.rand(3, 2), torch.rand(5, 2)
X_u_ = g.v2u(X_v, aggr="mean")
X_v_ = g.u2v(X_u, aggr="mean")
```
### Learning on High-Order Structures
On hypergraph structures, you can smooth a given vertex features with HGNN's Laplacian matrix by:
```python
import torch
import dhg
hg = dhg.random.hypergraph_Gnm(5, 4)
X = torch.rand(5, 2)
X_ = hg.smoothing_with_HGNN(X)
```
On hypergraph structures, you can pass messages from vertex to hyperedge with `mean` aggregation by:
```python
import torch
import dhg
hg = dhg.random.hypergraph_Gnm(5, 4)
X = torch.rand(5, 2)
Y_ = hg.v2e(X, aggr="mean")
```
Then, you can pass messages from hyperedge to vertex with `mean` aggregation by:
```python
X_ = hg.e2v(Y_, aggr="mean")
```
Or, you can pass messages from vertex set to vertex set with `mean` aggregation by:
```python
X_ = hg.v2v(X, aggr="mean")
```
## Examples
### Building the Convolution Layer of GCN
```python
class GCNConv(nn.Module):
def __init__(self,):
super().__init__()
...
self.reset_parameters()
def forward(self, X: torch.Tensor, g: dhg.Graph) -> torch.Tensor:
# apply the trainable parameters ``theta`` to the input ``X``
X = self.theta(X)
# smooth the input ``X`` with the GCN's Laplacian
X = g.smoothing_with_GCN(X)
X = F.relu(X)
return X
```
### Building the Convolution Layer of GAT
```python
class GATConv(nn.Module):
def __init__(self,):
super().__init__()
...
self.reset_parameters()
def forward(self, X: torch.Tensor, g: dhg.Graph) -> torch.Tensor:
# apply the trainable parameters ``theta`` to the input ``X``
X = self.theta(X)
# compute attention weights for each edge
x_for_src = self.atten_src(X)
x_for_dst = self.atten_dst(X)
e_atten_score = x_for_src[g.e_src] + x_for_dst[g.e_dst]
e_atten_score = F.leaky_relu(e_atten_score).squeeze()
# apply ``e_atten_score`` to each edge in the graph ``g``, aggragete neighbor messages
# with ``softmax_then_sum``, and perform vertex->vertex message passing in graph
# with message passing function ``v2v()``
X = g.v2v(X, aggr="softmax_then_sum", e_weight=e_atten_score)
X = F.elu(X)
return X
```
### Building the Convolution Layer of HGNN
```python
class HGNNConv(nn.Module):
def __init__(self,):
super().__init__()
...
self.reset_parameters()
def forward(self, X: torch.Tensor, hg: dhg.Hypergraph) -> torch.Tensor:
# apply the trainable parameters ``theta`` to the input ``X``
X = self.theta(X)
# smooth the input ``X`` with the HGNN's Laplacian
X = hg.smoothing_with_HGNN(X)
X = F.relu(X)
return X
```
### Building the Convolution Layer of HGNN $^+$
```python
class HGNNPConv(nn.Module):
def __init__(self,):
super().__init__()
...
self.reset_parameters()
def forward(self, X: torch.Tensor, hg: dhg.Hypergraph) -> torch.Tensor:
# apply the trainable parameters ``theta`` to the input ``X``
X = self.theta(X)
# perform vertex->hyperedge->vertex message passing in hypergraph
# with message passing function ``v2v``, which is the combination
# of message passing function ``v2e()`` and ``e2v()``
X = hg.v2v(X, aggr="mean")
X = F.relu(X)
return X
```
## Datasets
Currently, we have added the following datasets:
- **[Cora](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Cora.html#dhg.data.Cora)**: A citation network dataset for vertex classification task.
- **[PubMed](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Pubmed.html#dhg.data.Pubmed)**: A citation network dataset for vertex classification task.
- **[Citeseer](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Citeseer.html#dhg.data.Citeseer)**: A citation network dataset for vertex classification task.
- **[BlogCatalog](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.BlogCatalog.html#dhg.data.BlogCatalog)**: A social network dataset for vertex classification task.
- **[Flickr](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Flickr.html#dhg.data.Flickr)**: A social network dataset for vertex classification task.
- **[Github](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Github.html#dhg.data.Github)**: A collaboration network dataset for vertex classification task.
- **[Facebook](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Facebook.html#dhg.data.Facebook)**: A social network dataset for vertex classification task.
- **[MovieLens1M](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.MovieLens1M.html#dhg.data.MovieLens1M)**: A movie dataset for user-item recommendation task.
- **[AmazonBook](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.AmazonBook.html#dhg.data.AmazonBook)**: An Amazon dataset for user-item recommendation task.
- **[Yelp2018](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Yelp2018.html#dhg.data.Yelp2018)**: A restaurant review dataset for user-item recommendation task.
- **[Gowalla](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Gowalla.html#dhg.data.Gowalla)**: A location's feedback dataset for user-item recommendation task.
- **[TecentBiGraph](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.TencentBiGraph.html#dhg.data.TencentBiGraph)**: A social network dataset for vertex classification task.
- **[CoraBiGraph](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CoraBiGraph.html#dhg.data.CoraBiGraph)**: A citation network dataset for vertex classification task.
- **[PubmedBiGraph](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.PubmedBiGraph.html#dhg.data.PubmedBiGraph)**: A citation network dataset for vertex classification task.
- **[CiteseerBiGraph](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CiteseerBiGraph.html#dhg.data.CiteseerBiGraph)**: A citation network dataset for vertex classification task.
- **[Cooking200](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Cooking200.html#dhg.data.Cooking200)**: A cooking recipe dataset for vertex classification task.
- **[CoauthorshipCora](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CoauthorshipCora.html#dhg.data.CoauthorshipCora)**: A citation network dataset for vertex classification task.
- **[CoauthorshipDBLP](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CoauthorshipDBLP.html#dhg.data.CoauthorshipDBLP)**: A citation network dataset for vertex classification task.
- **[CocitationCora](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CocitationCora.html#dhg.data.CocitationCora)**: A citation network dataset for vertex classification task.
- **[CocitationPubmed](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CocitationCiteseer.html#dhg.data.CocitationCiteseer)**: A citation network dataset for vertex classification task.
- **[CocitationCiteseer](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CocitationPubmed.html#dhg.data.CocitationPubmed)**: A citation network dataset for vertex classification task.
- **[YelpRestaurant](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.YelpRestaurant.html#dhg.data.YelpRestaurant)**: A restaurant-review network dataset for vertex classification task.
- **[WalmartTrips](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.WalmartTrips.html#dhg.data.WalmartTrips)**: A user-product network dataset for vertex classification task.
- **[HouseCommittees](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.HouseCommittees.html#dhg.data.HouseCommittees)**: A committee network dataset for vertex classification task.
- **[News20](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.News20.html#dhg.data.News20)**: A newspaper network dataset for vertex classification task.
- **[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.
## Metrics
### Classification Metrics
- **[Accuracy](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.classification.accuracy)**: Calculates the accuracy of the predictions.
- **[F1-Score](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.classification.f1_score)**: Calculates the F1-score of the predictions.
- **[Confusion Matrix](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.classification.confusion_matrix)**: Calculates the confusion matrix of the predictions.
### Recommender Metrics
- **[Precision@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.recommender.precision)**: Calculates the precision@k of the predictions.
- **[Recall@k](https://deephypergraph.readthedocs.io/en/latest/_modules/dhg/metrics/recommender.html#recall)**: Calculates the recall@k of the predictions.
- **[NDCG@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.recommender.ndcg)**: Calculates the normalized discounted cumulative gain@k of the predictions.
### Retrieval Metrics
- **[Precision@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.precision)**: Calculates the precision@k of the predictions.
- **[Recall@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.recall)**: Calculates the recall@k of the predictions.
- **[mAP@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.map)**: Calculates the mAP@k of the predictions.
- **[NDCG@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.ndcg)**: Calculates the normalized Discounted Cumulative Gain@k of the predictions.
- **[mRR@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.mrr)**: Calculates the mean Reciprocal Rank@k of the predictions.
- **[PR-Curve](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.pr_curve)**: Calculates the precision-recall curve of the predictions.
## Implemented Models
### On Low-Order Structures
- **[GCN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.GCN.html#dhg.models.GCN)** model of [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/pdf/1609.02907) paper (ICLR 2017).
- **[GraphSAGE](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.GraphSAGE.html#dhg.models.GraphSAGE)** model of [Inductive Representation Learning on Large Graphs](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) paper (NeurIPS 2017).
- **[GAT](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.GAT.html#dhg.models.GAT)** model of [Graph Attention Networks](https://arxiv.org/pdf/1710.10903) paper (ICLR 2018).
- **[GIN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.GIN.html#dhg.models.GIN)** model of [How Powerful are Graph Neural Networks?](https://arxiv.org/pdf/1810.00826) paper (ICLR 2019).
- **[NGCF](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.NGCF.html#dhg.models.NGCF)** model of [Neural Graph Collaborative Filtering](https://arxiv.org/pdf/1905.08108) paper (SIGIR 2019).
- **[LightGCN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.LightGCN.html#dhg.models.LightGCN)** model of [LightGCN: Lightweight Graph Convolutional Networks](https://arxiv.org/pdf/2002.02126) paper (SIGIR 2020).
- **[BGNN-Adv](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.BGNN_Adv.html#dhg.models.BGNN_Adv)** model of [Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs](https://arxiv.org/pdf/1906.11994.pdf) paper (TNNLS 2020).
- **[BGNN-MLP](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.BGNN_MLP.html#dhg.models.BGNN_MLP)** model of [Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs](https://arxiv.org/pdf/1906.11994.pdf) paper (TNNLS 2020).
### On High-Order Structures
- **[HGNN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.HGNN.html#dhg.models.HGNN)** model of [Hypergraph Neural Networks](https://arxiv.org/pdf/1809.09401) paper (AAAI 2019).
- **[HGNN+](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.HGNNP.html#dhg.models.HGNNP)** model of [HGNN+: General Hypergraph Neural Networks](https://ieeexplore.ieee.org/document/9795251) paper (IEEE T-PAMI 2022).
- **[HyperGCN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.HyperGCN.html#dhg.models.HyperGCN)** model of [HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs](https://papers.nips.cc/paper/2019/file/1efa39bcaec6f3900149160693694536-Paper.pdf) paper (NeurIPS 2019).
- **[DHCF](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.DHCF.html#dhg.models.DHCF)** model of [Dual Channel Hypergraph Collaborative Filtering](https://dl.acm.org/doi/10.1145/3394486.3403253) paper (KDD 2020).
- **[HNHN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.HNHN.html#dhg.models.HNHN)** model of [HNHN: Hypergraph Networks with Hyperedge Neurons](https://arxiv.org/pdf/2006.12278.pdf) paper (ICML 2020).
- **[UniGCN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.UniGCN.html#dhg.models.UniGCN)** model of [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://arxiv.org/pdf/2105.00956.pdf) paper (IJCAI 2021).
- **[UniGAT](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.UniGAT.html#dhg.models.UniGAT)** model of [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://arxiv.org/pdf/2105.00956.pdf) paper (IJCAI 2021).
- **[UniSAGE](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.UniSAGE.html#dhg.models.UniSAGE)** model of [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://arxiv.org/pdf/2105.00956.pdf) paper (IJCAI 2021).
- **[UniGIN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.UniGIN.html#dhg.models.UniGIN)** model of [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://arxiv.org/pdf/2105.00956.pdf) paper (IJCAI 2021).
## Citing
If you find **DHG** is useful in your research, please consider citing:
```
@article{gao2022hgnn,
title={HGNN $\^{}+ $: General Hypergraph Neural Networks},
author={Gao, Yue and Feng, Yifan and Ji, Shuyi and Ji, Rongrong},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2022},
publisher={IEEE}
}
```
```
@inproceedings{feng2019hypergraph,
title={Hypergraph neural networks},
author={Feng, Yifan and You, Haoxuan and Zhang, Zizhao and Ji, Rongrong and Gao, Yue},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={33},
number={01},
pages={3558--3565},
year={2019}
}
```
## The DHG Team
DHG is developed by DHG's core team including [Yifan Feng](http://fengyifan.site/), [Xinwei Zhang](https://github.com/zhangxwww), [Jielong Yan](https://github.com/JasonYanjl), [Shuyi Ji](), [Yue Gao](http://moon-lab.tech/), and [Qionghai Dai](https://ysg.ckcest.cn/html/details/8058/index.html). It is maintained by the [iMoon-Lab](http://moon-lab.tech/), Tsinghua University. You can contact us at [email](mailto:evanfeng97@gmail.com).
## License
DHG uses Apache License 2.0.
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"description": "<p align=\"center\">\n <img src=\"https://deephypergraph.com/logo_DHG.png\" height=\"200\">\n</p>\n\n\n[](https://pypi.org/project/dhg/)\n[](https://deephypergraph.com/)\n[](https://deephypergraph.readthedocs.io/)\n[](https://pepy.tech/project/dhg)\n[](https://visitor-badge.glitch.me/)\n[](LICENSE)\n<!-- [](https://github.com/psf/black) -->\n<!-- [](https://pypi.org/project/dhg/) -->\n\n\n**[Website](https://deephypergraph.com/)** | **[Documentation](https://deephypergraph.readthedocs.io/)** | **[Tutorials](https://deephypergraph.readthedocs.io/en/latest/tutorial/overview.html)** | **[\u4e2d\u6587\u6587\u6863](https://deephypergraph.readthedocs.io/en/latest/zh/overview.html)** | **[Official Examples](https://deephypergraph.readthedocs.io/en/latest/examples/vertex_cls/index.html)** | **[Discussions](https://github.com/iMoonLab/DeepHypergraph/discussions)**\n\n\n## News\n- 2022-12-28 -> **v0.9.3** is now available! More datasets and operations of hypergraph are included!\n- 2022-12-28 -> **v0.9.3** \u6b63\u5f0f\u53d1\u5e03\uff01 \u5305\u542b\u66f4\u591a\u6570\u636e\u96c6\u548c\u8d85\u56fe\u64cd\u4f5c\uff01\n- 2022-09-25 -> **v0.9.2** is now available! More datasets, SOTA models, and visualizations are included!\n- 2022-09-25 -> **v0.9.2** \u6b63\u5f0f\u53d1\u5e03\uff01 \u5305\u542b\u66f4\u591a\u6570\u636e\u96c6\u3001\u6700\u65b0\u6a21\u578b\u548c\u53ef\u89c6\u5316\u529f\u80fd\uff01\n- 2022-08-25 -> DHG's first version **v0.9.1** is now available! \n- 2022-08-25 -> DHG\u7684\u7b2c\u4e00\u4e2a\u7248\u672c **v0.9.1** \u6b63\u5f0f\u53d1\u5e03\uff01\n\n\n**DHG** *(DeepHypergraph)* is a deep learning library built upon [PyTorch](https://pytorch.org) for learning with both Graph Neural Networks and Hypergraph Neural Networks. It is a general framework that supports both low-order and high-order message passing like **from vertex to vertex**, **from vertex in one domain to vertex in another domain**, **from vertex to hyperedge**, **from hyperedge to vertex**, **from vertex set to vertex set**.\n\nIt supports a wide variety of structures like low-order structures (graph, directed graph, bipartite graph, etc.), high-order structures (hypergraph, etc.). Various spectral-based operations (like Laplacian-based smoothing) and spatial-based operations (like message psssing from domain to domain) are integrated inside different structures. It provides multiple common metrics for performance evaluation on different tasks. Many state-of-the-art models are implemented and can be easily used for research. We also provide various visualization tools for both low-order structures and high-order structures. \n\nIn 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.\n\n\n\n\n\n* [Hightlights](#highlights)\n* [Installation](#installation)\n* [Quick Start](#quick-start)\n* [Examples](#examples)\n* [Datasets](#datasets)\n* [Metrics](#metrics)\n* [Implemented Models](#implemented-models)\n\n\n---------------------------------------------------------------\n\n## Highlights\n\n- **Support High-Order Message Passing on Structure**: \nDHG supports pair-wise message passing on the graph structure and beyond-pair-wise message passing on the hypergraph structure.\n\n- **Shared Ecosystem with Pytorch Framework**:\nDHG is built upon Pytorch, and any Pytorch-based models can be integrated into DHG. If you are familiar with Pytorch, you can easily use DHG.\n\n- **Powerful API for Designing GNNs and HGNNs**:\nDHG provides various Laplacian matrices and message passing functions to help build your spectral/spatial-based models, respectively.\n\n- **Visualization of Graphs and Hypergraphs**\nDHG provides a powerful visualization tool for graph and hypergraph. You can easily visualize the structure of your graph and hypergraph.\n\n- **Bridge the Gap between Graphs and Hypergraphs**:\nDHG provides functions to build hypergraph from graph and build graph from hypergraph. Maybe promoting the graph to hypergraph can exploit those potential high-order connections and improve the performance of your model.\n\n- **Attach Spectral/Spatial-Based Operations to Structure**:\nIn DHG, those Laplacian matrices and message passing functions are attached to the graph/hypergraph structure. As soon as you build a structure with DHG, those functions will be ready to be used in the process of building your model.\n\n- **Comprehensive, Flexible, and Convenience**:\nDHG provides random graph/hypergraph generators, various state-of-the-art graph/hypergraph convolutional layers and models, various public graph/hypergraph datasets, and various evaluation metrics.\n\n- **Support Tuning Structure and Model with Auto-ML**:\nThe Optuna library endows DHG with the Auto-ML ability. DHG supports automatically searching the optimal configurations for the construction of graph/hypergraph structure and the optimal hyper-parameters for your model and training.\n\n## Installation\n\n\nCurrent, the stable version of **DHG** is 0.9.3. You can install it with ``pip`` as follows:\n\n```python\npip install dhg\n```\n\nYou can also try the nightly version (0.9.4) of **DHG** library with ``pip`` as follows:\n\n```python\npip install git+https://github.com/iMoonLab/DeepHypergraph.git\n```\n\nNightly version is the development version of **DHG**. It may include the lastest SOTA methods and datasets, but it can also be unstable and not fully tested. \nIf you find any bugs, please report it to us in [GitHub Issues](https://github.com/iMoonLab/DeepHypergraph/issues).\n\n## Quick Start\n\n### Visualization\n\nYou 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)\n\n\n\n\n```python\nimport matplotlib.pyplot as plt\nimport dhg\n# draw a graph\ng = dhg.random.graph_Gnm(10, 12)\ng.draw()\n# draw a hypergraph\nhg = dhg.random.hypergraph_Gnm(10, 8)\nhg.draw()\n# show figures\nplt.show()\n```\n\n\n\n```python\nimport matplotlib.pyplot as plt\nimport dhg\n# draw a directed graph\ng = dhg.random.digraph_Gnm(12, 18)\ng.draw()\n# draw a bipartite graph\ng = dhg.random.bigraph_Gnm(30, 40, 20)\ng.draw()\n# show figures\nplt.show()\n```\n\n### Learning on Low-Order Structures\n\nOn graph structures, you can smooth a given vertex features with GCN's Laplacian matrix by:\n\n```python\nimport torch\nimport dhg\ng = dhg.random.graph_Gnm(5, 8)\nX = torch.rand(5, 2)\nX_ = g.smoothing_with_GCN(X)\n```\n\nOn graph structures, you can pass messages from vertex to vertex with `mean` aggregation by:\n\n```python\nimport torch\nimport dhg\ng = dhg.random.graph_Gnm(5, 8)\nX = torch.rand(5, 2)\nX_ = g.v2v(X, aggr=\"mean\")\n```\n\nOn directed graph structures, you can pass messages from vertex to vertex with `mean` aggregation by:\n\n```python\nimport torch\nimport dhg\ng = dhg.random.digraph_Gnm(5, 8)\nX = torch.rand(5, 2)\nX_ = g.v2v(X, aggr=\"mean\")\n```\n\nOn bipartite graph structures, you can smoothing vertex features with GCN's Laplacian matrix by:\n\n```python\nimport torch\nimport dhg\ng = dhg.random.bigraph_Gnm(3, 5, 8)\nX_u, X_v = torch.rand(3, 2), torch.rand(5, 2)\nX = torch.cat([X_u, X_v], dim=0)\nX_ = g.smoothing_with_GCN(X, aggr=\"mean\")\n```\n\nOn bipartite graph structures, you can pass messages from vertex in `U` set to vertex in `V` set by `mean` aggregation by:\n\n```python\nimport torch\nimport dhg\ng = dhg.random.bigraph_Gnm(3, 5, 8)\nX_u, X_v = torch.rand(3, 2), torch.rand(5, 2)\nX_u_ = g.v2u(X_v, aggr=\"mean\")\nX_v_ = g.u2v(X_u, aggr=\"mean\")\n```\n\n### Learning on High-Order Structures\n\nOn hypergraph structures, you can smooth a given vertex features with HGNN's Laplacian matrix by:\n\n```python\nimport torch\nimport dhg\nhg = dhg.random.hypergraph_Gnm(5, 4)\nX = torch.rand(5, 2)\nX_ = hg.smoothing_with_HGNN(X)\n```\n\nOn hypergraph structures, you can pass messages from vertex to hyperedge with `mean` aggregation by:\n\n```python\nimport torch\nimport dhg\nhg = dhg.random.hypergraph_Gnm(5, 4)\nX = torch.rand(5, 2)\nY_ = hg.v2e(X, aggr=\"mean\")\n```\nThen, you can pass messages from hyperedge to vertex with `mean` aggregation by:\n\n```python\nX_ = hg.e2v(Y_, aggr=\"mean\")\n```\nOr, you can pass messages from vertex set to vertex set with `mean` aggregation by:\n\n```python\nX_ = hg.v2v(X, aggr=\"mean\")\n```\n\n## Examples\n\n### Building the Convolution Layer of GCN\n\n```python\nclass GCNConv(nn.Module):\n def __init__(self,):\n super().__init__()\n ...\n self.reset_parameters()\n\n def forward(self, X: torch.Tensor, g: dhg.Graph) -> torch.Tensor:\n # apply the trainable parameters ``theta`` to the input ``X`` \n X = self.theta(X)\n # smooth the input ``X`` with the GCN's Laplacian\n X = g.smoothing_with_GCN(X)\n X = F.relu(X)\n return X\n```\n\n### Building the Convolution Layer of GAT\n\n```python\nclass GATConv(nn.Module):\n def __init__(self,):\n super().__init__()\n ...\n self.reset_parameters()\n\n def forward(self, X: torch.Tensor, g: dhg.Graph) -> torch.Tensor:\n # apply the trainable parameters ``theta`` to the input ``X``\n X = self.theta(X)\n # compute attention weights for each edge\n x_for_src = self.atten_src(X)\n x_for_dst = self.atten_dst(X)\n e_atten_score = x_for_src[g.e_src] + x_for_dst[g.e_dst]\n e_atten_score = F.leaky_relu(e_atten_score).squeeze()\n # apply ``e_atten_score`` to each edge in the graph ``g``, aggragete neighbor messages\n # with ``softmax_then_sum``, and perform vertex->vertex message passing in graph \n # with message passing function ``v2v()``\n X = g.v2v(X, aggr=\"softmax_then_sum\", e_weight=e_atten_score)\n X = F.elu(X)\n return X\n```\n\n### Building the Convolution Layer of HGNN\n\n```python\nclass HGNNConv(nn.Module):\n def __init__(self,):\n super().__init__()\n ...\n self.reset_parameters()\n\n def forward(self, X: torch.Tensor, hg: dhg.Hypergraph) -> torch.Tensor:\n # apply the trainable parameters ``theta`` to the input ``X``\n X = self.theta(X)\n # smooth the input ``X`` with the HGNN's Laplacian\n X = hg.smoothing_with_HGNN(X)\n X = F.relu(X)\n return X\n```\n\n\n### Building the Convolution Layer of HGNN $^+$\n\n```python\nclass HGNNPConv(nn.Module):\n def __init__(self,):\n super().__init__()\n ...\n self.reset_parameters()\n\n def forward(self, X: torch.Tensor, hg: dhg.Hypergraph) -> torch.Tensor:\n # apply the trainable parameters ``theta`` to the input ``X``\n X = self.theta(X)\n # perform vertex->hyperedge->vertex message passing in hypergraph\n # with message passing function ``v2v``, which is the combination\n # of message passing function ``v2e()`` and ``e2v()``\n X = hg.v2v(X, aggr=\"mean\")\n X = F.relu(X)\n return X\n```\n\n\n## Datasets\n\nCurrently, we have added the following datasets:\n\n- **[Cora](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Cora.html#dhg.data.Cora)**: A citation network dataset for vertex classification task.\n\n- **[PubMed](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Pubmed.html#dhg.data.Pubmed)**: A citation network dataset for vertex classification task.\n\n- **[Citeseer](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Citeseer.html#dhg.data.Citeseer)**: A citation network dataset for vertex classification task.\n\n- **[BlogCatalog](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.BlogCatalog.html#dhg.data.BlogCatalog)**: A social network dataset for vertex classification task.\n\n- **[Flickr](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Flickr.html#dhg.data.Flickr)**: A social network dataset for vertex classification task.\n\n- **[Github](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Github.html#dhg.data.Github)**: A collaboration network dataset for vertex classification task.\n\n- **[Facebook](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Facebook.html#dhg.data.Facebook)**: A social network dataset for vertex classification task.\n\n- **[MovieLens1M](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.MovieLens1M.html#dhg.data.MovieLens1M)**: A movie dataset for user-item recommendation task.\n\n- **[AmazonBook](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.AmazonBook.html#dhg.data.AmazonBook)**: An Amazon dataset for user-item recommendation task.\n\n- **[Yelp2018](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Yelp2018.html#dhg.data.Yelp2018)**: A restaurant review dataset for user-item recommendation task.\n\n- **[Gowalla](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Gowalla.html#dhg.data.Gowalla)**: A location's feedback dataset for user-item recommendation task.\n\n- **[TecentBiGraph](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.TencentBiGraph.html#dhg.data.TencentBiGraph)**: A social network dataset for vertex classification task.\n\n- **[CoraBiGraph](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CoraBiGraph.html#dhg.data.CoraBiGraph)**: A citation network dataset for vertex classification task.\n\n- **[PubmedBiGraph](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.PubmedBiGraph.html#dhg.data.PubmedBiGraph)**: A citation network dataset for vertex classification task.\n\n- **[CiteseerBiGraph](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CiteseerBiGraph.html#dhg.data.CiteseerBiGraph)**: A citation network dataset for vertex classification task.\n\n- **[Cooking200](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Cooking200.html#dhg.data.Cooking200)**: A cooking recipe dataset for vertex classification task.\n\n- **[CoauthorshipCora](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CoauthorshipCora.html#dhg.data.CoauthorshipCora)**: A citation network dataset for vertex classification task.\n\n- **[CoauthorshipDBLP](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CoauthorshipDBLP.html#dhg.data.CoauthorshipDBLP)**: A citation network dataset for vertex classification task.\n\n- **[CocitationCora](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CocitationCora.html#dhg.data.CocitationCora)**: A citation network dataset for vertex classification task.\n\n- **[CocitationPubmed](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CocitationCiteseer.html#dhg.data.CocitationCiteseer)**: A citation network dataset for vertex classification task.\n\n- **[CocitationCiteseer](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.CocitationPubmed.html#dhg.data.CocitationPubmed)**: A citation network dataset for vertex classification task.\n\n- **[YelpRestaurant](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.YelpRestaurant.html#dhg.data.YelpRestaurant)**: A restaurant-review network dataset for vertex classification task.\n\n- **[WalmartTrips](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.WalmartTrips.html#dhg.data.WalmartTrips)**: A user-product network dataset for vertex classification task.\n\n- **[HouseCommittees](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.HouseCommittees.html#dhg.data.HouseCommittees)**: A committee network dataset for vertex classification task.\n\n- **[News20](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.News20.html#dhg.data.News20)**: A newspaper network dataset for vertex classification task.\n\n- **[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.\n\n## Metrics\n\n### Classification Metrics\n\n- **[Accuracy](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.classification.accuracy)**: Calculates the accuracy of the predictions.\n\n- **[F1-Score](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.classification.f1_score)**: Calculates the F1-score of the predictions.\n\n- **[Confusion Matrix](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.classification.confusion_matrix)**: Calculates the confusion matrix of the predictions.\n\n### Recommender Metrics\n\n- **[Precision@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.recommender.precision)**: Calculates the precision@k of the predictions.\n\n- **[Recall@k](https://deephypergraph.readthedocs.io/en/latest/_modules/dhg/metrics/recommender.html#recall)**: Calculates the recall@k of the predictions.\n\n- **[NDCG@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.recommender.ndcg)**: Calculates the normalized discounted cumulative gain@k of the predictions.\n\n### Retrieval Metrics\n\n- **[Precision@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.precision)**: Calculates the precision@k of the predictions.\n\n- **[Recall@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.recall)**: Calculates the recall@k of the predictions.\n\n- **[mAP@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.map)**: Calculates the mAP@k of the predictions.\n\n- **[NDCG@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.ndcg)**: Calculates the normalized Discounted Cumulative Gain@k of the predictions.\n\n- **[mRR@k](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.mrr)**: Calculates the mean Reciprocal Rank@k of the predictions.\n\n- **[PR-Curve](https://deephypergraph.readthedocs.io/en/latest/api/metrics.html#dhg.metrics.retrieval.pr_curve)**: Calculates the precision-recall curve of the predictions.\n\n## Implemented Models\n\n### On Low-Order Structures\n\n- **[GCN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.GCN.html#dhg.models.GCN)** model of [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/pdf/1609.02907) paper (ICLR 2017).\n\n- **[GraphSAGE](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.GraphSAGE.html#dhg.models.GraphSAGE)** model of [Inductive Representation Learning on Large Graphs](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) paper (NeurIPS 2017).\n\n- **[GAT](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.GAT.html#dhg.models.GAT)** model of [Graph Attention Networks](https://arxiv.org/pdf/1710.10903) paper (ICLR 2018).\n\n- **[GIN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.GIN.html#dhg.models.GIN)** model of [How Powerful are Graph Neural Networks?](https://arxiv.org/pdf/1810.00826) paper (ICLR 2019).\n\n- **[NGCF](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.NGCF.html#dhg.models.NGCF)** model of [Neural Graph Collaborative Filtering](https://arxiv.org/pdf/1905.08108) paper (SIGIR 2019).\n\n- **[LightGCN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.LightGCN.html#dhg.models.LightGCN)** model of [LightGCN: Lightweight Graph Convolutional Networks](https://arxiv.org/pdf/2002.02126) paper (SIGIR 2020).\n\n- **[BGNN-Adv](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.BGNN_Adv.html#dhg.models.BGNN_Adv)** model of [Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs](https://arxiv.org/pdf/1906.11994.pdf) paper (TNNLS 2020).\n\n- **[BGNN-MLP](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.BGNN_MLP.html#dhg.models.BGNN_MLP)** model of [Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs](https://arxiv.org/pdf/1906.11994.pdf) paper (TNNLS 2020).\n\n\n### On High-Order Structures\n\n- **[HGNN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.HGNN.html#dhg.models.HGNN)** model of [Hypergraph Neural Networks](https://arxiv.org/pdf/1809.09401) paper (AAAI 2019).\n\n- **[HGNN+](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.HGNNP.html#dhg.models.HGNNP)** model of [HGNN+: General Hypergraph Neural Networks](https://ieeexplore.ieee.org/document/9795251) paper (IEEE T-PAMI 2022).\n\n- **[HyperGCN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.HyperGCN.html#dhg.models.HyperGCN)** model of [HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs](https://papers.nips.cc/paper/2019/file/1efa39bcaec6f3900149160693694536-Paper.pdf) paper (NeurIPS 2019).\n\n- **[DHCF](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.DHCF.html#dhg.models.DHCF)** model of [Dual Channel Hypergraph Collaborative Filtering](https://dl.acm.org/doi/10.1145/3394486.3403253) paper (KDD 2020).\n\n- **[HNHN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.HNHN.html#dhg.models.HNHN)** model of [HNHN: Hypergraph Networks with Hyperedge Neurons](https://arxiv.org/pdf/2006.12278.pdf) paper (ICML 2020).\n\n- **[UniGCN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.UniGCN.html#dhg.models.UniGCN)** model of [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://arxiv.org/pdf/2105.00956.pdf) paper (IJCAI 2021).\n\n- **[UniGAT](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.UniGAT.html#dhg.models.UniGAT)** model of [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://arxiv.org/pdf/2105.00956.pdf) paper (IJCAI 2021).\n\n- **[UniSAGE](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.UniSAGE.html#dhg.models.UniSAGE)** model of [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://arxiv.org/pdf/2105.00956.pdf) paper (IJCAI 2021).\n\n- **[UniGIN](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.models.UniGIN.html#dhg.models.UniGIN)** model of [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://arxiv.org/pdf/2105.00956.pdf) paper (IJCAI 2021).\n\n\n\n## Citing\nIf you find **DHG** is useful in your research, please consider citing:\n\n```\n@article{gao2022hgnn,\n title={HGNN $\\^{}+ $: General Hypergraph Neural Networks},\n author={Gao, Yue and Feng, Yifan and Ji, Shuyi and Ji, Rongrong},\n journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},\n year={2022},\n publisher={IEEE}\n}\n```\n```\n@inproceedings{feng2019hypergraph,\n title={Hypergraph neural networks},\n author={Feng, Yifan and You, Haoxuan and Zhang, Zizhao and Ji, Rongrong and Gao, Yue},\n booktitle={Proceedings of the AAAI conference on artificial intelligence},\n volume={33},\n number={01},\n pages={3558--3565},\n year={2019}\n}\n```\n\n## The DHG Team\n\nDHG is developed by DHG's core team including [Yifan Feng](http://fengyifan.site/), [Xinwei Zhang](https://github.com/zhangxwww), [Jielong Yan](https://github.com/JasonYanjl), [Shuyi Ji](), [Yue Gao](http://moon-lab.tech/), and [Qionghai Dai](https://ysg.ckcest.cn/html/details/8058/index.html). It is maintained by the [iMoon-Lab](http://moon-lab.tech/), Tsinghua University. You can contact us at [email](mailto:evanfeng97@gmail.com).\n\n\n## License\n\nDHG uses Apache License 2.0.",
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