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--- a/README.md
+++ b/README.md
@@ -1,9 +1,22 @@
 # Awesome-model-inversion-attack
 
+### Survey
+Arxiv 2022 - A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability.
+[[paper]](https://arxiv.org/pdf/2204.08570.pdf)
+
+Arxiv 2022 - Trustworthy Graph Neural Networks: Aspects, Methods and Trends.
+[[paper]](https://arxiv.org/pdf/2205.07424.pdf)
+
+Arxiv 2022 - A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.
+[[paper]](https://arxiv.org/pdf/2205.10014.pdf)
 
 
 ### Graph Learning
 
+IJCAI 2021 - GraphMI: Extracting Private Graph Data from Graph Neural Networks.
+[[paper]](https://arxiv.org/abs/2106.02820)
+[[code]](https://github.com/zaixizhang/GraphMI)
+
 WWW 2022 - Learning Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes.
 [[paper]](https://dl.acm.org/doi/pdf/10.1145/3485447.3511975?casa_token=Xsle4t9cLdcAAAAA:Gmij-qWaTJ2esGVa-yzKNHqVOMzYyaIgdNcgGmEzHrVyMdwwf9idn3qBjkhCcQeRTvbAkaT6OxiwXsk)
 
@@ -24,3 +37,4 @@ Arxiv 2022 - Private Graph Extraction via Feature Explanations.
 
 
 <!--   [[paper]]()   -->
+<!--   [[code]]()   -->