Graph-Neural-Networks.md

@@ -15,7 +15,69 @@ Commonly the below terms are referred to the same thing.
 
 ## Awesome Papers
 ### Survey Papers
+1. **Introduction to Graph Neural Networks.** Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, 2020. [book](https://www.morganclaypool.com/doi/10.2200/S00980ED1V01Y202001AIM045)
 
+    *Zhiyuan Liu, Jie Zhou.* 
+
+1. **Graph Neural Networks: A Review of Methods and Applications.** AI Open 2020. [paper](https://doi.org/10.1016/j.aiopen.2021.01.001)
+   
+    *Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun.* 
+
+1. **A Comprehensive Survey on Graph Neural Networks.** arxiv 2019. [paper](https://arxiv.org/pdf/1901.00596.pdf)
+
+    *Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu.*
+    
+1. **Adversarial Attack and Defense on Graph Data: A Survey.** arxiv 2018. [paper](https://arxiv.org/pdf/1812.10528.pdf)
+
+    *Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Bo Li.* 
+
+1. **Deep Learning on Graphs: A Survey.** arxiv 2018. [paper](https://arxiv.org/pdf/1812.04202.pdf)
+
+    *Ziwei Zhang, Peng Cui, Wenwu Zhu.*
+
+1. **Relational Inductive Biases, Deep Learning, and Graph Networks.** arxiv 2018. [paper](https://arxiv.org/pdf/1806.01261.pdf)
+
+    *Battaglia, Peter W and Hamrick, Jessica B and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and others.*
+
+1. **Geometric Deep Learning: Going beyond Euclidean data.** IEEE SPM 2017. [paper](https://arxiv.org/pdf/1611.08097.pdf)
+
+    *Bronstein, Michael M and Bruna, Joan and LeCun, Yann and Szlam, Arthur and Vandergheynst, Pierre.*
+
+1. **Computational Capabilities of Graph Neural Networks.** IEEE TNN 2009. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4703190)
+
+    *Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele.*
+
+1. **Neural Message Passing for Quantum Chemistry.** ICML 2017. [paper](https://arxiv.org/pdf/1704.01212.pdf)
+
+    *Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E.*
+
+1. **Non-local Neural Networks.** CVPR 2018. [paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Non-Local_Neural_Networks_CVPR_2018_paper.pdf)
+
+    *Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming.*
+
+1. **The Graph Neural Network Model.** IEEE TNN 2009. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4700287)
+
+    *Scarselli, Franco and Gori, Marco and Tsoi, Ah Chung and Hagenbuchner, Markus and Monfardini, Gabriele.*
+    
+1. **Benchmarking Graph Neural Networks.** arxiv 2020. [paper](https://arxiv.org/pdf/2003.00982.pdf)
+
+    *Dwivedi, Vijay Prakash and Joshi, Chaitanya K. and Laurent, Thomas and Bengio, Yoshua and Bresson, Xavier.*
+
+1. **Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey.** arxiv 2020. [paper](https://arxiv.org/abs/2005.07496)
+
+    *Skarding, Joakim and Gabrys, Bogdan and Musial, Katarzyna.*
+
+1. **Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks.** arxiv 2020. [paper](https://arxiv.org/abs/2002.11867)
+
+    *Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Chang-Tien Lu.*
+    
+1. **Explainability in Graph Neural Networks: A Taxonomic Survey.** arxiv 2020. [paper](https://arxiv.org/abs/2012.15445)
+
+    *Hao Yuan, Haiyang Yu, Shurui Gui, Shuiwang Ji.*
+
+1. **Self-Supervised Learning of Graph Neural Networks: A unified view.** TPAMI 2022. [paper](https://ieeexplore.ieee.org/abstract/document/9764632)
+
+    *Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhangyang Wang, Shuiwang Ji.*
 ### Technical Papers
 ### Evaluation Papers