Fetching the paper…
Reading the bibliography…
We study training of Graph Neural Networks (GNNs) for large-scale graphs.
Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J. Smola, and Zheng Zhang. 2019 · 1909
Earlier work this paper cites.
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, and Kevin Murphy. 2021 · 2005
Earlier work this paper cites.
GraphChi: Large-Scale Graph Computation on Just a PC. In 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 12) . USENIX Association, Hollywood, CA, 31–46
Aapo Kyrola, Guy Blelloch, and Carlos Guestrin. 2012 · 2012
Earlier work this paper cites.
Translating Embeddings for Modeling Multi-relational Data. In Advances in Neural Information Processing Systems , C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger (Eds.), Vol. 26. Curran Associates, Inc
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
SNAP Datasets: Stanford large network dataset collection
Jure Leskovec and Andrej Krevl. 2014 · 2014
Earlier work this paper cites.
Web Data Commons - Hyperlink Graphs
Robert Meusel, Oliver Lehmberg, Christian Bizer, and Sebastiano Vigna. 2014 · 2014
Earlier work this paper cites.
Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014 · 2014
Earlier work this paper cites.
One trillion edges: Graph processing at facebook-scale
Avery Ching, Sergey Edunov, Maja Kabiljo, Dionysios Logothetis, and Sambavi Muthukrishnan. 2015 · 2015
Earlier work this paper cites.
Variance reduced stochastic gradient descent with neighbors
Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien, and Brian McWilliams. 2015 · 2015
Earlier work this paper cites.
Scalability! But at what { \{ COST } \} ?. In 15th Workshop on Hot Topics in Operating Systems (HotOS { \{ XV } \} )
Frank McSherry, Michael Isard, and Derek G Murray. 2015 · 2015
Earlier work this paper cites.
Representing Text for Joint Embedding of Text and Knowledge Bases. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Lisbon, Portugal, 1499–1509
Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. 2015 · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Mosaic: Processing a Trillion-Edge Graph on a Single Machine. In Proceedings of the Twelfth European Conference on Computer Systems (Belgrade, Serbia) (EuroSys ’17) . Association for Computing Machinery, New York, NY, USA, 527–543
Steffen Maass, Changwoo Min, Sanidhya Kashyap, Woonhak Kang, Mohan Kumar, and Taesoo Kim. 2017 · 2017
Earlier work this paper cites.
Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao. 2018 · 2018
Earlier work this paper cites.
Freebase Data Dumps
Google. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks. In International Conference on Learning Representations
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen. 2018 · 2018
Cited alongside, same era.
Cluster-GCN
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
Cited alongside, same era.
Graph neural networks for social recommendation. In The World Wide Web Conference . 417–426
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen. 2019 · 2019
Cited alongside, same era.
Random Shuffling Beats SGD after Finite Epochs. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 2624–2633
Jeff Haochen and Suvrit Sra. 2019 · 2019
Cited alongside, same era.
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs. In 2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3) . IEEE Computer Society, Los Alamitos, CA, USA, 36–44
D. Zheng, C. Ma, M. Wang, J. Zhou, Q. Su, X. Song, Q. Gan, Z. Zhang, and G. Karypis. 2020a · 2020
Later among the works it cites.
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs. In 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 . Association for Computing Machinery, 289–299
Jialin Dong, Da Zheng, Lin F Yang, and George Karypis. 2021 · 2021
Later among the works it cites.
P3: Distributed Deep Graph Learning at Scale. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21) . USENIX Association, 551–568
Swapnil Gandhi and Anand Padmanabha Iyer. 2021 · 2021
Later among the works it cites.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. 2021 · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pytorch-biggraph: A large scale graph embedding system
Adam Lerer, Ledell Wu, Jiajun Shen, Timothee Lacroix, Luca Wehrstedt, Abhijit Bose, and Alex Peysakhovich. 2019 · 2019
Cited alongside, same era.
Estimating node importance in knowledge graphs using graph neural networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 596–606
Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, and Christos Faloutsos. 2019 · 2019
Cited alongside, same era.
Layer-dependent importance sampling for training deep and large graph convolutional networks
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, and Quanquan Gu. 2019 · 2019
Cited alongside, same era.
GOSH: Embedding Big Graphs on Small Hardware. In 49th International Conference on Parallel Processing - ICPP (Edmonton, AB, Canada) (ICPP ’20) . Association for Computing Machinery, New York, NY, USA, Article 4, 11 pages
Taha Atahan Akyildiz, Amro Alabsi Aljundi, and Kamer Kaya. 2020 · 2020
Cited alongside, same era.
Random Reshuffling is Not Always Better. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 5957–5967
Christopher M De Sa. 2020 · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc. In Proceedings of Machine Learning and Systems , I. Dhillon, D. Papailiopoulos, and V. Sze (Eds.), Vol. 2. 187–198
Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Accelerating graph sampling for graph machine learning using GPUs. In Proceedings of the Sixteenth European Conference on Computer Systems . 311–326
Abhinav Jangda, Sandeep Polisetty, Arjun Guha, and Marco Serafini. 2021 · 2021
Later among the works it cites.
Bgl: Gpu-efficient gnn training by optimizing graph data i/o and preprocessing
Tianfeng Liu, Yangrui Chen, Dan Li, Chuan Wu, Yibo Zhu, Jun He, Yanghua Peng, Hongzheng Chen, Hongzhi Chen, and Chuanxiong Guo. 2021 · 2021
Later among the works it cites.
Marius: Learning Massive Graph Embeddings on a Single Machine. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21) . 533–549
Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, and Shivaram Venkataraman. 2021 · 2021
Later among the works it cites.
Discrete Graph Structure Learning for Forecasting Multiple Time Series. In Proceedings of International Conference on Learning Representations
Chao Shang and Jie Chen. 2021 · 2021
Later among the works it cites.
Improved Partitioning Graph Embedding Framework for Small Cluster. In Knowledge Science, Engineering and Management , Han Qiu, Cheng Zhang, Zongming Fei, Meikang Qiu, and Sun-Yuan Kung (Eds.). Springer International Publishing, Cham, 203–215
Ding Sun, Zhen Huang, Dongsheng Li, Xiangyu Ye, and Yilin Wang. 2021 · 2021
Later among the works it cites.
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21) . USENIX Association, 495–514
John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, Keval Vora, Ravi Netravali, Miryung Kim, and Guoqing Harry Xu. 2021 · 2021
Later among the works it cites.
Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale. In SIGMOD 2022
Ihab F Ilyas, Theodoros Rekatsinas, Vishnu Konda, Jeffrey Pound, Xiaoguang Qi, and Mohamed Soliman. 2022 · 2022
Closest in time.
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining
Tim Kaler, Nickolas Stathas, Anne Ouyang, Alexandros-Stavros Iliopoulos, Tao Schardl, Charles E Leiserson, and Jie Chen. 2022 · 2022
Closest in time.
GNNLab: a factored system for sample-based GNN training over GPUs. In Proceedings of the Seventeenth European Conference on Computer Systems . 417–434
Jianbang Yang, Dahai Tang, Xiaoniu Song, Lei Wang, Qiang Yin, Rong Chen, Wenyuan Yu, and Jingren Zhou. 2022 · 2022
Closest in time.
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs
Da Zheng, Xiang Song, Chengru Yang, Qidong Su, Minjie Wang, Chao Ma, and George Karypis. 2022 · 2022
Closest in time.
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, Jinjun Xiong, Eiman Ebrahimi, Deming Chen, and Wen-mei Hwu. 2021a · 2087
Closest in time.
AliGraph: A Comprehensive Graph Neural Network Platform
Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, and Jingren Zhou. 2019 · 2094
Closest in time.