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README.md

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 - [IJCAI 2021] [**Automated Machine Learning on Graphs: A Survey**](https://arxiv.org/abs/2103.00742).
 Ziwei Zhang, Xin Wang, Wenwu Zhu.
 
+<p class="center">
+ <img src="img/dual-nas.PNG" width="60%">
+ <br>
+ <em>Source: Probabilistic Dual Network Architecture Search on Graphs</em>
+</p>
+
 ## Large-scale Graphs and Sampling Techniques
 - [KDD 2019] [**Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks**](https://arxiv.org/abs/1905.07953). Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh.
 - [ICLR 2020] [**GraphSAINT: Graph Sampling Based Inductive Learning Method**](https://arxiv.org/abs/1907.04931). Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna.
 - [KDD 2020] [**Scaling Graph Neural Networks with Approximate PageRank**](https://arxiv.org/abs/2007.01570). Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann.
 - [ICML 2021] [**GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings**](https://arxiv.org/abs/2106.05609). Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec.
 - [ICLR 2021] [**Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning**](https://arxiv.org/abs/2102.04350). Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan.
 
+<p class="center">
+ <img src="img/graph-saint.PNG" width="60%">
+ <br>
+ <em>Source: GraphSAINT: Graph Sampling Based Inductive Learning Method</em>
+</p>
+
 ## Low Precision and Quantized GNNs
 - [EuroMLSys 2021] [**Learned Low Precision Graph Neural Networks**](https://arxiv.org/abs/2009.09232). Yiren Zhao, Duo Wang, Daniel Bates, Robert Mullins, Mateja Jamnik, Pietro Lio.
 - [ICLR 2021] [**Degree-Quant: Quantization-Aware Training for Graph Neural Networks**](https://arxiv.org/abs/2008.05000). Shyam A. Tailor, Javier Fernandez-Marques, Nicholas D. Lane.
 - [CVPR 2021] [**Binary Graph Neural Networks**](https://arxiv.org/abs/2012.15823). Mehdi Bahri, Gaétan Bahl, Stefanos Zafeiriou.
 
+<p class="center">
+ <img src="img/degree-quant.PNG" width="60%">
+ <br>
+ <em>Source: Degree-Quant: Quantization-Aware Training for Graph Neural Networks</em>
+</p>
+
 ## Knowledge Distillation for GNNs
 - [CVPR 2020] [**Distilling Knowledge from Graph Convolutional Networks**](https://arxiv.org/abs/2003.10477). Yiding Yang, Jiayan Qiu, Mingli Song, Dacheng Tao, Xinchao Wang.
 - [WWW 2021] [**Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework**](https://arxiv.org/abs/2103.02885). Cheng Yang, Jiawei Liu, Chuan Shi.
 
+<p class="center">
+ <img src="img/distillation.PNG" width="60%">
+ <br>
+ <em>Source: Distilling Knowledge from Graph Convolutional Networks</em>
+</p>
+
 ## Hardware Acceleration of GNNs
 - [IPDPS 2019] [**Accurate, Efficient and Scalable Graph Embedding**](https://arxiv.org/abs/1810.11899). Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna.
 - [IEEE TC 2020] [**EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks**](https://arxiv.org/abs/1909.00155). Shengwen Liang, Ying Wang, Cheng Liu, Lei He, Huawei Li, Xiaowei Li.
 - [FPGA 2020] [**GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms**](https://arxiv.org/abs/2001.02498). Hanqing Zeng, Viktor Prasanna.
 - [IEEE CAD 2021] [**Rubik: A Hierarchical Architecture for Efficient Graph Learning**](https://arxiv.org/abs/2009.12495). Xiaobing Chen, Yuke Wang, Xinfeng Xie, Xing Hu, Abanti Basak, Ling Liang, Mingyu Yan, Lei Deng, Yufei Ding, Zidong Du, Yunji Chen, Yuan Xie.
 - [ACM Computing 2021] [**Computing Graph Neural Networks: A Survey from Algorithms to Accelerators**](https://arxiv.org/abs/2010.00130). Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso, Eduard Alarcón.
 
+<p class="center">
+ <img src="img/computing-gnns.PNG" width="60%">
+ <br>
+ <em>Source: Computing Graph Neural Networks: A Survey from Algorithms to Accelerators</em>
+</p>
+
 ## Industrial Applications and Systems
 - [KDD 2018] [**Graph Convolutional Neural Networks for Web-Scale Recommender Systems**](https://arxiv.org/abs/1806.01973). Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec.
 - [VLDB 2019] [**AliGraph: A Comprehensive Graph Neural Network Platform**](https://arxiv.org/abs/1902.08730). Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou.