Merge branch 'main' of github.com:iMoonLab/DeepHypergraph into main

Author: zhangxwww

README.md

@@ -15,14 +15,12 @@
 
 **[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)**
 
-<p align="center">
-The v0.9.2 will be released soon, which will include many structure visualizations!<br>
-我们很快将发布v0.9.2版本，其中包含了各种关联结构的可视化！
-</p>
 
 ## News
-- 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！
+- 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**.
@@ -71,13 +69,13 @@ The Optuna library endows DHG with the Auto-ML ability. DHG supports automatical
 ## Installation
 
 
-Current, the stable version of **DHG** is 0.9.1. You can install it with ``pip`` as follows:
+Current, the stable version of **DHG** is 0.9.2. You can install it with ``pip`` as follows:
 
 ```python
 pip install dhg
 ```
 
-You can also try the nightly version (0.9.2) of **DHG** library with ``pip`` as follows:
+You can also try the nightly version (0.9.3) of **DHG** library with ``pip`` as follows:
 
 ```python
 pip install git+https://github.com/iMoonLab/DeepHypergraph.git
@@ -88,6 +86,41 @@ If you find any bugs, please report it to us in [GitHub Issues](https://github.c
 
 ## 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)
+
+
+![Visualization of graph and hypergraph](docs/source/_static/img/readme_graph_hypergraph.png)
+
+```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()
+```
+
+![Visualization of directed graph and bipartite graph](docs/source/_static/img/readme_digraph_bigraph.png)
+
+```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:
@@ -269,7 +302,13 @@ Currently, we have added the following datasets:
 
 - **[Citeseer](https://deephypergraph.readthedocs.io/en/latest/generated/dhg.data.Citeseer.html#dhg.data.Citeseer)**: 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.
+- **[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.
 
@@ -279,6 +318,34 @@ Currently, we have added the following datasets:
 
 - **[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.
+
 ## Metrics
 
 ### Classification Metrics
@@ -327,6 +394,10 @@ Currently, we have added the following datasets:
 
 - **[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
 
@@ -336,9 +407,17 @@ Currently, we have added the following datasets:
 
 - **[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).
 
-- **[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).
+- **[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).
 
 
 

docs/source/_static/img/readme_digraph_bigraph.png

@@ -12,12 +12,6 @@ DHG Overview
    \part{English}
    \chapter{DHG Overview}
 
-.. only:: not latex
-
-   .. attention:: 
-
-      We are rushing the documentation and tutorials, which will be updated twice a day!
-
 
 **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 
@@ -35,7 +29,8 @@ can help you automatically tune the hyper-parameters of your models in training
 
 **News**
 
-- *2022-08-25*  ->  The **v0.9.1 release** is now available!
+- 2022-09-25 -> The **v0.9.2** is now available! More datasets, SOTA models, and visualizations are included!
+- 2022-08-25 -> The **v0.9.1** is now available!
 
 **Citing**
 

docs/source/_static/img/readme_graph_hypergraph.png

@@ -1,12 +1,6 @@
 DHG简介
 ==============
 
-.. only:: not latex
-
-   .. attention:: 
-      
-      我们正在加快文档和教程的更新，每天会在午饭和晚饭期间进行两次更新！
-
 **DHG** (*DeepHypergraph*) 是基于 `PyTorch <https://pytorch.org/>`_ 的深度学习包，
 可用于图神经网络以及超图神经网络。
 其支持 **从顶点到顶点**、**从一个域的顶点到另一个域的顶点**、 **从顶点到超边**,、**从超边到顶点**、**从顶点集到顶点集** 等低阶或高阶信息传递的通用框架。
@@ -21,7 +15,8 @@ DHG简介
 
 **新闻**
 
-- *2022-08-25*  ->   DHG的第一个版本 **v0.9.1** 正式发布！
+- 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的第一个版本 **v0.9.1** 正式发布！
 
 **引用**