Efficient Graph Neural Networks - a curated list of papers and projects

Project README

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!

**Accompanying Blogpost**: chaitjo.com/post/efficient-gnns

- [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. - [ICLR 2021]
**On Graph Neural Networks versus Graph-Augmented MLPs**. Lei Chen, Zhengdao Chen, Joan Bruna. - [ICML 2021]
**Training Graph Neural Networks with 1000 Layers**. Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun.

*Source: Simplifying Graph Convolutional Networks*

- [IJCAI 2020]
**GraphNAS: Graph Neural Architecture Search with Reinforcement Learning**. Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, Yue Hu. - [AAAI 2021 Workshop]
**Probabilistic Dual Network Architecture Search on Graphs**. Yiren Zhao, Duo Wang, Xitong Gao, Robert Mullins, Pietro Lio, Mateja Jamnik. - [IJCAI 2021]
**Automated Machine Learning on Graphs: A Survey**. Ziwei Zhang, Xin Wang, Wenwu Zhu.

*Source: Probabilistic Dual Network Architecture Search on Graphs*

- [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. - [CVPR 2020]
**L**. Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen.^{2}-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks - [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.

*Source: GraphSAINT: Graph Sampling Based Inductive Learning Method*

- [EuroMLSys 2021]
**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.

*Source: Degree-Quant: Quantization-Aware Training for Graph Neural Networks*

- [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. - [IJCAI 2021]
**On Self-Distilling Graph Neural Network**. Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang. - [IJCAI 2021]
**Graph-Free Knowledge Distillation for Graph Neural Networks**. Xiang Deng, Zhongfei Zhang. - [ArXiv 2021]
**On Representation Knowledge Distillation for Graph Neural Networks**. Chaitanya K. Joshi, Fayao Liu, Xu Xun, Jie Lin, Chuan-Sheng Foo.

*Source: On Representation Knowledge Distillation for Graph Neural Networks*

- [IPDPS 2019]
**Accurate, Efficient and Scalable Graph Embedding**. 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**. Shengwen Liang, Ying Wang, Cheng Liu, Lei He, Huawei Li, Xiaowei Li. - [FPGA 2020]
**GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms**. Hanqing Zeng, Viktor Prasanna. - [IEEE CAD 2021]
**Rubik: A Hierarchical Architecture for Efficient Graph Learning**. 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**. Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso, Eduard Alarcón.

*Source: Computing Graph Neural Networks: A Survey from Algorithms to Accelerators*

- [PyG]
**PyTorch Geometric**. - [DGL]
**Deep Graph Library**. - [NeurIPS 2020]
**Open Graph Benchmark: Datasets for Machine Learning on Graphs**. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec. - [KDD Cup 2021]
**OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs**Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec. - [CIKM 2021]
**PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models**. Benedek Rozemberczki, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, Ferenc Beres, Guzmán López, Nicolas Collignon, Rik Sarkar.

*Source: OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs*

- [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. - [KDD 2020]
**PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest**Aditya Pal, Chantat Eksombatchai, Yitong Zhou, Bo Zhao, Charles Rosenberg, Jure Leskovec. - [CIKM 2020]
**P-Companion: A Principled Framework for Diversified Complementary Product Recommendation**Junheng Hao, Tong Zhao, Jin Li, Xin Luna Dong, Christos Faloutsos, Yizhou Sun, and Wei Wang. - [CIKM 2021]
**ETA Prediction with Graph Neural Networks in Google Maps**. Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, Peter W. Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, Petar Veličković.

*Source: Graph Convolutional Neural Networks for Web-Scale Recommender Systems*

Open Source Agenda is not affiliated with "Awesome Efficient Gnn" Project. README Source: chaitjo/awesome-efficient-gnn

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