From e9df28574bc4e95eeb1519159826fb0724be4534 Mon Sep 17 00:00:00 2001
From: Generall <465383637@qq.com>
Date: Thu, 29 Dec 2022 16:29:41 +0800
Subject: [PATCH] Update README.md

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 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b83fea8..09bed98 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ Feel free to open <a href="https://github.com/THUMNLab/AutoGL/issues">issues</a>
 
 - 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 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_robust.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.