Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based tasks.
Metis: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices
George Karypis and Vipin Kumar · 1997
Earlier work this paper cites.
The pagerank citation ranking : Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd · 1999
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Earlier work this paper cites.
Stochastic training of graph convolutional networks with variance reduction
Jianfei Chen, Jun Zhu, and Le Song · 2018
Earlier work this paper cites.
Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
Earlier work this paper cites.
Large-scale learnable graph convolutional networks
Hongyang Gao, Zhengyang Wang, and Shuiwang Ji · 2018
Earlier work this paper cites.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Earlier work this paper cites.
Adaptive sampling towards fast graph representation learning
Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang · 2018
Earlier work this paper cites.
Graph attention networks
Zhiyuan Liu and Jie Zhou · 2018
Earlier work this paper cites.
Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec · 2018
Earlier work this paper cites.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
Earlier work this paper cites.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Earlier work this paper cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Earlier work this paper cites.
Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, et al · 2019
Earlier work this paper cites.
Simplifying graph convolutional networks
Felix Wu, AmauriHolandade Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and KilianQ. Weinberger · 2019
Earlier work this paper cites.
Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Scaling graph neural networks with approximate pagerank
Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann · 2020
Earlier work this paper cites.
Scalable graph neural networks via bidirectional propagation
Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, and Ji-Rong Wen · 2020
Earlier work this paper cites.
Minimal variance sampling with provable guarantees for fast training of graph neural networks
Weilin Cong, Rana Forsati, Mahmut Kandemir, and Mehrdad Mahdavi · 2020
Earlier work this paper cites.
Sgquant: Squeezing the last bit on graph neural networks with specialized quantization
Boyuan Feng, Yuke Wang, Xu Li, Shu Yang, Xueqiao Peng, and Yufei Ding · 2020
Earlier work this paper cites.
Sign: Scalable inception graph neural networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, and Federico Monti · 2020
Earlier work this paper cites.
Combining label propagation and simple models out-performs graph neural networks
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin Benson · 2020
Earlier work this paper cites.
Improving the accuracy, scalability, and performance of graph neural networks with roc
Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken · 2020
Earlier work this paper cites.
Engn: A high-throughput and energy-efficient accelerator for large graph neural networks
Shengwen Liang, Ying Wang, Cheng Liu, Lei He, LI Huawei, Dawen Xu, and Xiaowei Li · 2020
Earlier work this paper cites.
Pruning algorithms to accelerate convolutional neural networks for edge applications: A survey
Jiayi Liu, Samarth Tripathi, Unmesh Kurup, and Mohak Shah · 2020
Earlier work this paper cites.
Bandit samplers for training graph neural networks
Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, and Qi Yuan · 2020
Earlier work this paper cites.
Binary neural networks: A survey
Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, and Nicu Sebe · 2020
Earlier work this paper cites.
Degree-quant: Quantization-aware training for graph neural networks
Shyam Anil Tailor, Javier Fernandez-Marques, and Nicholas Donald Lane · 2020
Earlier work this paper cites.
Tinygnn: Learning efficient graph neural networks
Bencheng Yan, Chaokun Wang, Gaoyang Guo, and Yunkai Lou · 2020
Earlier work this paper cites.
Distilling knowledge from graph convolutional networks
Yiding Yang, Jiayan Qiu, Mingli Song, Dacheng Tao, and Xinchao Wang · 2020
Earlier work this paper cites.
Learned low precision graph neural networks
Yiren Zhao, Duo Wang, Daniel Bates, Robert Mullins, Mateja Jamnik, and Pietro Lio · 2020
Cited alongside, same era.
Binary graph neural networks
Mehdi Bahri, Gaétan Bahl, and Stefanos Zafeiriou · 2021
Cited alongside, same era.
Ripple walk training: A subgraph-based training framework for large and deep graph neural network
Jiyang Bai, Yuxiang Ren, and Jiawei Zhang · 2021
Cited alongside, same era.
A unified lottery ticket hypothesis for graph neural networks
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang · 2021
Cited alongside, same era.
Graph-free knowledge distillation for graph neural networks
Xiang Deng and Zhongfei Zhang · 2021
Cited alongside, same era.
Vq-gnn: A universal framework to scale up graph neural networks using vector quantization
Graph condensation via receptive field distribution matching
Mengyang Liu, Shanchuan Li, Xinshi Chen, and Le Song · 2022
Later among the works it cites.
Survey on graph neural network acceleration: An algorithmic perspective
Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, and Yuan Xie · 2022
Later among the works it cites.
Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, et al · 2022
Later among the works it cites.
Learning mlps on graphs: A unified view of effectiveness, robustness, and efficiency
Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, and Nitesh Chawla · 2022
Later among the works it cites.
Searching lottery tickets in graph neural networks: A dual perspective
Kun Wang, Yuxuan Liang, Pengkun Wang, Xu Wang, Pengfei Gu, Junfeng Fang, and Yang Wang · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, and Tom Goldstein · 2021
Cited alongside, same era.
Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings
Matthias Fey, Jan E Lenssen, Frank Weichert, and Jure Leskovec · 2021
Cited alongside, same era.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
Cited alongside, same era.
Graph-mlp: Node classification without message passing in graph
Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, and Yue Gao · 2021
Cited alongside, same era.
Graph condensation for graph neural networks
Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah · 2021
Cited alongside, same era.
Meta-aggregator: Learning to aggregate for 1-bit graph neural networks
Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, and Dacheng Tao · 2021
Cited alongside, same era.
Graph signal processing, graph neural network and graph learning on biological data: a systematic review
Rui Li, Xin Yuan, Mohsen Radfar, Peter Marendy, Wei Ni, Terence J O’Brien, and Pablo M Casillas-Espinosa · 2021
Cited alongside, same era.
Later among the works it cites.
Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui · 2022
Later among the works it cites.
Geometric knowledge distillation: Topology compression for graph neural networks
Chenxiao Yang, Qitian Wu, and Junchi Yan · 2022
Later among the works it cites.
Early-bird gcns: Graph-network co-optimization towards more efficient gcn training and inference via drawing early-bird lottery tickets
Haoran You, Zhihan Lu, Zijian Zhou, Yonggan Fu, and Yingyan Lin · 2022
Later among the works it cites.
Pasca: A graph neural architecture search system under the scalable paradigm
Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui · 2022
Later among the works it cites.
Nafs: A simple yet tough-to-beat baseline for graph representation learning
Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, and Bin Cui · 2022
Later among the works it cites.
Graph attention multi-layer perceptron
Wentao Zhang, Ziqi Yin, Zeang Sheng, Yang Li, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, and Bin Cui · 2022
Later among the works it cites.
Hierarchical graph transformer with adaptive node sampling
Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee · 2022
Later among the works it cites.
A 2 Q \mathrm{A}^{2}\mathrm{Q} : Aggregation-aware quantization for graph neural networks
Zeyu Zhu, Fanrong Li, Zitao Mo, Qinghao Hu, Gang Li, Zejian Liu, Xiaoyao Liang, and Jian Cheng · 2022
Later among the works it cites.
Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications
Rui Bing, Guan Yuan, Mu Zhu, Fanrong Meng, Huifang Ma, and Shaojie Qiao · 2023
Later among the works it cites.
Bitgnn: Unleashing the performance potential of binary graph neural networks on gpus
Jou-An Chen, Hsin-Hsuan Sung, Xipeng Shen, Sutanay Choudhury, and Ang Li · 2023
Later among the works it cites.
Haar wavelet feature compression for quantized graph convolutional networks
Moshe Eliasof, Benjamin J Bodner, and Eran Treister · 2023
Later among the works it cites.
Node-wise diffusion for scalable graph learning
Keke Huang, Jing Tang, Juncheng Liu, Renchi Yang, and Xiaokui Xiao · 2023
Later among the works it cites.
Scalable decoupling graph neural network with feature-oriented optimization
Ningyi Liao, Dingheng Mo, Siqiang Luo, Xiang Li, and Pengcheng Yin · 2023
Later among the works it cites.
Graph condensation via eigenbasis matching
Yang Liu, Deyu Bo, and Chuan Shi · 2023
Later among the works it cites.
Lmc: Fast training of gnns via subgraph sampling with provable convergence
Zhihao Shi, Xize Liang, and Jie Wang · 2023
Later among the works it cites.
Propagate & distill: Towards effective graph learners using propagation-embracing mlps
Yong-Min Shin and Won-Yong Shin · 2023
Later among the works it cites.
Knowledge distillation on graphs: A survey
Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, and Nitesh V Chawla · 2023
Later among the works it cites.
The snowflake hypothesis: Training deep gnn with one node one receptive field
Kun Wang, Guohao Li, Shilong Wang, Guibin Zhang, Kai Wang, Yang You, Xiaojiang Peng, Yuxuan Liang, and Yang Wang · 2023
Later among the works it cites.
Fast graph condensation with structure-based neural tangent kernel
Lin Wang, Wenqi Fan, Jiatong Li, Yao Ma, and Qing Li · 2023
Later among the works it cites.
Low-bit quantization for deep graph neural networks with smoothness-aware message propagation
Shuang Wang, Bahaeddin Eravci, Rustam Guliyev, and Hakan Ferhatosmanoglu · 2023
Later among the works it cites.
Quantifying the knowledge in gnns for reliable distillation into mlps
Lirong Wu, Haitao Lin, Yufei Huang, and Stan Z Li · 2023
Later among the works it cites.
Kernel ridge regression-based graph dataset distillation
Zhe Xu, Yuzhong Chen, Menghai Pan, Huiyuan Chen, Mahashweta Das, Hao Yang, and Hanghang Tong · 2023
Later among the works it cites.
Grapes: Learning to sample graphs for scalable graph neural networks
Taraneh Younesian, Thiviyan Thanapalasingam, Emile van Krieken, Daniel Daza, and Peter Bloem · 2023
Later among the works it cites.
A survey on graph neural network acceleration: Algorithms, systems, and customized hardware
Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan, Zijie Huang, Ziniu Hu, Yewen Wang, Jason Cong, Yizhou Sun, et al · 2023
Later among the works it cites.
Towards data-centric graph machine learning: Review and outlook
Xin Zheng, Yixin Liu, Zhifeng Bao, Meng Fang, Xia Hu, Alan Wee-Chung Liew, and Shirui Pan · 2023
Later among the works it cites.
Structure-free graph condensation: From large-scale graphs to condensed graph-free data
Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, and Shirui Pan · 2023
Later among the works it cites.
Graph condensation for inductive node representation learning
Xinyi Gao, Tong Chen, Yilong Zang, Wentao Zhang, Quoc Viet Hung Nguyen, Kai Zheng, and Hongzhi Yin · 2024
Closest in time.
Vqgraph: Graph vector-quantization for bridging gnns and mlps
Ling Yang, Ye Tian, Minkai Xu, Zhongyi Liu, Shenda Hong, Wei Qu, Wentao Zhang, Bin Cui, Muhan Zhang, and Jure Leskovec · 2024
Closest in time.