Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification, regression, and recommendation tasks.
η \eta production in nucleon-nucleon collisions
T Vetter, A Engel, T Biro, and U Mosel · 1991
Earlier work this paper cites.
Non-local graph neural networks
Meng Liu, Zhengyang Wang, and Shuiwang Ji · 2005
Earlier work this paper cites.
Understanding and resolving performance degradation in graph convolutional networks
Kuangqi Zhou, Yanfei Dong, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, and Jiashi Feng · 2006
Earlier work this paper cites.
Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2007
Earlier work this paper cites.
Tackling over-smoothing for general graph convolutional networks
Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, and Junzhou Huang · 2008
Earlier work this paper cites.
Addressing cold-start problem in recommendation systems
Xuan Nhat Lam, Thuc Vu, Trong Duc Le, and Anh Duc Duong · 2008
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 R Benson · 2010
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 R Benson · 2010
Earlier work this paper cites.
A graph-based friend recommendation system using genetic algorithm
Nitai B Silva, Ren Tsang, George DC Cavalcanti, and Jyh Tsang · 2010
Earlier work this paper cites.
On self-distilling graph neural network
Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, and Junzhou Huang · 2011
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Recommendation system in e-commerce websites: A graph based approached
Shakila Shaikh, Sheetal Rathi, and Prachi Janrao · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Cited alongside, same era.
Graph embedding techniques, applications, and performance: A survey
Palash Goyal and Emilio Ferrara · 2018
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Cited alongside, same era.
Deepergcn: All you need to train deeper gcns
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem · 2020
Later among the works it cites.
Meta-learning on heterogeneous information networks for cold-start recommendation
Yuanfu Lu, Yuan Fang, and Chuan Shi · 2020
Later among the works it cites.
Graph neural networks exponentially lose expressive power for node classification
Kenta Oono and Taiji Suzuki · 2020
Later among the works it cites.
Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
Later among the works it cites.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
Cited alongside, same era.
Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
Cited alongside, same era.
Break the ceiling: Stronger multi-scale deep graph convolutional networks
Sitao Luan, Mingde Zhao, Xiao-Wen Chang, and Doina Precup · 2019
Cited alongside, same era.
Revisiting graph neural networks: All we have is low-pass filters
Hoang NT and Takanori Maehara · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Cited alongside, same era.
Star-gcn: Stacked and reconstructed graph convolutional networks for recommender systems
Jiani Zhang, Xingjian Shi, Shenglin Zhao, and Irwin King · 2019
Cited alongside, same era.
Pairnorm: Tackling oversmoothing in gnns
Lingxiao Zhao and Leman Akoglu · 2019
Cited alongside, same era.
Hongwei Wang and Jure Leskovec · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Later among the works it cites.
Revisiting ”over-smoothing” in deep gcns
Chaoqi Yang, Ruijie Wang, Shuochao Yao, Shengzhong Liu, and Tarek Abdelzaher · 2020
Later among the works it cites.
Hongwei Zhang, Tijin Yan, Zenjun Xie, Yuanqing Xia, and Yuan Zhang · 2020
Later among the works it cites.
Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, and Hao Wang · 2021
Closest in time.
Pre-training graph neural networks for cold-start users and items representation
Bowen Hao, Jing Zhang, Hongzhi Yin, Cuiping Li, and Hong Chen · 2021
Closest in time.
Graph-mlp: Node classification without message passing in graph
Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, and Yue Gao · 2021
Closest in time.
Dissecting the diffusion process in linear graph convolutional networks
Yifei Wang, Yisen Wang, Jiansheng Yang, and Zhouchen Lin · 2021
Closest in time.
Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework
Cheng Yang, Jiawei Liu, and Chuan Shi · 2021
Closest in time.
Graph-less neural networks: Teaching old mlps new tricks via distillation
Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah · 2021
Closest in time.