THUMNLab
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@@ -14,17 +14,16 @@ Feel free to open <a href="https://github.com/THUMNLab/AutoGL/issues">issues</a>
 
 ## News!
 
-- 2022.12.29 New version! v0.4.0 is here!
-    - AutoGL can now be easily used in conjunction with __NAS-Bench-Graph__ ([paper](https://openreview.net/pdf?id=bBff294gqLp),[code](https://github.com/THUMNLab/NAS-Bench-Graph)), greatly speeding up the performance estimation process of GraphNAS algorithms.
-    - We have extended the graph __robustness__ algorithms in AutoGL, including structure engineering, robust GNN and robust GraphNAS. See [robustness tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_robustness.html) for more details.
-    - Graph __self-supervised learning__ is now supported! See [ssl tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_ssl_trainer.html) for more details.
-- 2022.4.19 New version v0.3.1！We have released Chinese tutorial for the first time!
-- 2021.12.31 New Version! v0.3.0-pre is here!
-    - AutoGL now support [__Deep Graph Library (DGL)__](https://www.dgl.ai/) backend to be interface-friendly for DGL users! All the homogeneous node classification task, link prediction task, and graph classification task are currently supported under DGL backend. AutoGL is also compatible with PyG 2.0 now.
-    - The __heterogeneous__ node classification tasks are now supported! See [hetero tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_hetero_node_clf.html) for more details.
-    - To make the library more flexible, the module `model` now supports __decoupled__ to two additional sub-modules named `encoder` and `decoder`. Under the __decoupled__ design, one `encoder` can be used to solve all kinds of tasks, relieving burdens for developing and user expanding/contributing.
-    - We enrich our supported [NAS algorithms](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_nas.html) such as [AutoAttend](https://proceedings.mlr.press/v139/guan21a.html), [GASSO](https://proceedings.neurips.cc/paper/2021/hash/8c9f32e03aeb2e3000825c8c875c4edd-Abstract.html), [hardware-aware algorithm](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/documentation/nas.html#autogl.module.nas.estimator.OneShotEstimator_HardwareAware), etc. 
-- 2021.07.11 New version! v0.2.0-pre is here! In this new version, AutoGL supports [neural architecture search (NAS)](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_nas.html) to customize architectures for the given datasets and tasks. AutoGL also supports [sampling](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_trainer.html#node-classification-with-sampling) now to perform tasks on large datasets, including node-wise sampling, layer-wise sampling, and sub-graph sampling. The link prediction task is now also supported! Learn more in our [tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/index.html).
+- 2022.12.29 New version! v0.4.0-pre is here!
+    - We have proposed __NAS-Bench-Graph__ ([paper](https://openreview.net/pdf?id=bBff294gqLp),[code](https://github.com/THUMNLab/NAS-Bench-Graph), [tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_nas_bench_graph.html)), the first NAS-benchmark for graphs published in NeurIPS'22. By using AutoGL together with NAS-Bench-Graph, the performance estimation process of GraphNAS algorithms can be greatly speeded up. 
+    - We have supported the graph __robustness__ algorithms in AutoGL, including graph structure engineering, robust GNNs and robust GraphNAS. See [robustness tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_robustness.html) for more details.
+    - We have supported graph __self-supervised learning__! See [self-supervised learning tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_ssl_trainer.html) for more details.
+- 2021.12.31 Version v0.3.0-pre is released
+    - Support [__Deep Graph Library (DGL)__](https://www.dgl.ai/) backend including homogeneous node classification, link prediction, and graph classification tasks. AutoGL is also compatible with PyG 2.0 now.
+    - Support __heterogeneous__ node classification! See [hetero tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_hetero_node_clf.html) .
+    - The module `model` now supports __decoupled__ to two additional sub-modules named `encoder` and `decoder`. Under the __decoupled__ design, one `encoder` can be used to solve all kinds of tasks.
+    - Enrich [NAS algorithms](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_nas.html) such as [AutoAttend](https://proceedings.mlr.press/v139/guan21a.html), [GASSO](https://proceedings.neurips.cc/paper/2021/hash/8c9f32e03aeb2e3000825c8c875c4edd-Abstract.html), [hardware-aware algorithm](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/documentation/nas.html#autogl.module.nas.estimator.OneShotEstimator_HardwareAware), etc. 
+- 2021.07.11 Version 0.2.0-pre is released, which supports [neural architecture search (NAS)](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_nas.html) to customize architectures, [sampling (http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_trainer.html#node-classification-with-sampling) to perform tasks on large datasets, and link prediction. 
 - 2021.04.16 Our survey paper about automated machine learning on graphs is accepted by IJCAI! See more [here](http://arxiv.org/abs/2103.00742).
 - 2021.04.10 Our paper [__AutoGL: A Library for Automated Graph Learning__](https://arxiv.org/abs/2104.04987) is accepted by _ICLR 2021 Workshop on Geometrical and Topological Representation Learning_! You can cite our paper following methods [here](#Cite).