GitHub - chaitjo/awesome-efficient-gnn: Efficient Graph Neural Networks - a cura...
source link: https://github.com/chaitjo/awesome-efficient-gnn
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
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.
- [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.
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.
- [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.
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
- [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.
Knowledge Distillation for GNNs
Hardware Acceleration of GNNs
- [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.
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.
Recommend
-
22
Graph Neural Networks: An overview Over the past decade, we’ve seen that Neural Networks can perform tremendously well in structured data like images and text. Most of the popular models like convolutional netw...
-
8
Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems...
-
32
Graph4NLP Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both
-
8
How to tap into India’s exploding gaming marketLearn how developers and studios of every size can successfully grow their titles in one of the fastest-growing markets in the world.
-
7
A Gentle Introduction to Graph Neural NetworksA Gentle Introduction to Graph Neural NetworksA Gentle Introduction to Graph Neural Networks Neural networks have been adapted to leverage the structure and properties of graphs. We exp...
-
6
GNNs Recipe Graph neural networks (GNNs) are rapidly advancing progress in ML for complex graph data applications. I've composed this concise recipe dedicated to students who are lookin to learn and keep up-to-date with GNNs. It's no...
-
5
LinkedIn creates PASS to tailor graph neural networks for social media Image Credit: TechTalks We are excited to bring Transform...
-
2
A Lagrangian Approach to Information Propagation in Graph Neural NetworksPublished in ECAI2020, 2020Recommended citation: Matteo Tiezzi, Giuseppe Marra, Stefano Melacci, Marco Maggini and Marco Gori (2020). "A Lagran...
-
5
Deep Constraint-based Propagation in Graph Neural NetworksPublished in TPAMI, 2021Recommended citation: Matteo Tiezzi, Giuseppe Marra, Stefano Melacci, Marco Maggini (2021). "Deep Constraint-based Propagation in Grap...
-
8
Case study: Efficient audio-based convolutional neural networks via filter pruning by Dr. Arshdeep Singh,...
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK