🚀 Awesome Efficient Graph Neural Networks

This is a curated list of must-read papers on efficient Graph Neural Networks and scalable Graph Representation Learning for real-world applications. Contributions for new papers and topics are welcome!

Efficient and Scalable GNN Architectures

• [ICML 2019] Simplifying Graph Convolutional Networks. Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger.
• [ICML 2020 Workshop] SIGN: Scalable Inception Graph Neural Networks. Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, Federico Monti.
• [ICLR 2021 Workshop] Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions. Shyam A. Tailor, Felix L. Opolka, Pietro Liò, Nicholas D. Lane.
• [ICML 2021] Training Graph Neural Networks with 1000 Layers. Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun.

Neural Architecture Search for GNNs

• [IJCAI 2020] GraphNAS: Graph Neural Architecture Search with Reinforcement Learning. Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, Yue Hu.
• [IJCAI 2021] Automated Machine Learning on Graphs: A Survey. Ziwei Zhang, Xin Wang, Wenwu Zhu.

Large-scale Graphs and Sampling Techniques

• [KDD 2019] Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh.
• [ICLR 2020] GraphSAINT: Graph Sampling Based Inductive Learning Method. Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna.
• [KDD 2020] Scaling Graph Neural Networks with Approximate PageRank. 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. Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec.
• [ICLR 2021] Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan.

Low Precision and Quantized GNNs

• [MLSys 2020] Learned Low Precision Graph Neural Networks. Yiren Zhao, Duo Wang, Daniel Bates, Robert Mullins, Mateja Jamnik, Pietro Lio.
• [ICLR 2021] Degree-Quant: Quantization-Aware Training for Graph Neural Networks. Shyam A. Tailor, Javier Fernandez-Marques, Nicholas D. Lane.
• [CVPR 2021] Binary Graph Neural Networks. Mehdi Bahri, Gaétan Bahl, Stefanos Zafeiriou.

Knowledge Distillation for GNNs

• [CVPR 2020] Distilling Knowledge from Graph Convolutional Networks. 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. Cheng Yang, Jiawei Liu, Chuan Shi.

Hardware Acceleration of GNNs

• [ACM Computing 2021] Computing Graph Neural Networks: A Survey from Algorithms to Accelerators. Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso, Eduard Alarcón.

Industrial Applications and Systems

• [KDD 2018] Graph Convolutional Neural Networks for Web-Scale Recommender Systems. Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec.
• [VLDB 2019] AliGraph: A Comprehensive Graph Neural Network Platform. Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, Jingren Zhou.