Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have already been widely applied in various graph mining tasks.
On the evolution of random graphs
Paul Erdos, Alfréd Rényi, et al · 1960
Earlier work this paper cites.
The PageRank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999
Earlier work this paper cites.
Finding frequent patterns in a large sparse graph
Michihiro Kuramochi and George Karypis. 2005 · 2005
Earlier work this paper cites.
Exploring network structure, dynamics, and function using NetworkX
Aric Hagberg, Pieter Swart, and Daniel S Chult. 2008 · 2008
Earlier work this paper cites.
Collective Classification in Network Data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
Earlier work this paper cites.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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.
Graph convolutional encoders for syntax-aware neural machine translation
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Sima’an. 2017 · 2017
Earlier work this paper cites.
Few-shot learning with graph neural networks
Victor Garcia and Joan Bruna. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs. In NIPS . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Geometric matrix completion with recurrent multi-graph neural networks
Federico Monti, Michael M Bronstein, and Xavier Bresson. 2017 · 2017
Earlier work this paper cites.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Earlier work this paper cites.
Knowledge distillation by on-the-fly native ensemble. In Proceedings of the 32nd International Conference on Neural Information Processing Systems . 7528–7538
Xu Lan, Xiatian Zhu, and Shaogang Gong. 2018 · 2018
Earlier work this paper cites.
Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 32
Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018 · 2018
Earlier work this paper cites.
Learning human-object interactions by graph parsing neural networks. In Proceedings of the European Conference on Computer Vision (ECCV) . 401–417
Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, and Song-Chun Zhu. 2018 · 2018
Earlier work this paper cites.
Deepinf: Modeling influence locality in large social networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18)
Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Representation learning on graphs with jumping knowledge networks. In International Conference on Machine Learning . PMLR, 5453–5462
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018 · 2018
Earlier work this paper cites.
A Generative Model For Electron Paths. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, and José Miguel Hernández-Lobato. 2019 · 2019
Earlier work this paper cites.
Retrosynthesis Prediction with Conditional Graph Logic Network. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada . 8870–8880
Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, and Le Song. 2019 · 2019
Earlier work this paper cites.
Graph Transformation Policy Network for Chemical Reaction Prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019 . 750–760
Kien Do, Truyen Tran, and Svetha Venkatesh. 2019 · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Measuring and improving the use of graph information in graph neural networks. In International Conference on Learning Representations
Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard TB Ma, Hongzhi Chen, and Ming-Chang Yang. 2019 · 2019
Cited alongside, same era.
Deepgcns: Can gcns go as deep as cnns?. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 9267–9276
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. 2019 · 2019
Cited alongside, same era.
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
Hoang NT and Takanori Maehara. 2019 · 2019
Cited alongside, same era.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019 · 2019
Cited alongside, same era.
Deep Graph Neural Networks with Shallow Subgraph Samplers
Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, and Ren Chen. 2020a · 2020
Later among the works it cites.
GraphSAINT: Graph Sampling Based Inductive Learning Method. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna. 2020b · 2020
Later among the works it cites.
Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2020 · 2020
Later among the works it cites.
PairNorm: Tackling Oversmoothing in GNNs. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net
Lingxiao Zhao and Leman Akoglu. 2020 · 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…
Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu. 2019 · 2019
Cited alongside, same era.
Simplifying graph convolutional networks. In International conference on machine learning . PMLR, 6861–6871
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019 · 2019
Cited alongside, same era.
A deeper graph neural network for recommender systems
Ruiping Yin, Kan Li, Guangquan Zhang, and Jie Lu. 2019 · 2019
Cited alongside, same era.
Attributed graph clustering via adaptive graph convolution. In 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 . International Joint Conferences on Artificial Intelligence, 4327–4333
Xiaotong Zhang, Han Liu, Qimai Li, and Xiao Ming Wu. 2019 · 2019
Cited alongside, same era.
A note on over-smoothing for graph neural networks
Chen Cai and Yusu Wang. 2020 · 2020
Cited alongside, same era.
Scalable Graph Neural Networks via Bidirectional Propagation
Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, and Ji-Rong Wen. 2020b · 2020
Cited alongside, same era.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020 · 2020
Cited alongside, same era.
Sign: Scalable inception graph neural networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, and Federico Monti. 2020 · 2020
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, 36–44
Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, and George Karypis. 2020 · 2020
Later among the works it cites.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020a · 2020
Later among the works it 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. 2020b · 2020
Later among the works it cites.
Towards deeper graph neural networks with differentiable group normalization
Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu. 2020c · 2020
Later among the works it cites.
Multi-Channel Graph Neural Networks. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020 . 1352–1358
Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu. 2020d · 2020
Later among the works it cites.
Directional Graph Networks. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event . 748–758
Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, and Pietro Lió. 2021 · 2021
Closest in time.
GRAND: Graph Neural Diffusion. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event . 1407–1418
Ben Chamberlain, James Rowbottom, Maria Gorinova, Michael M. Bronstein, Stefan Webb, and Emanuele Rossi. 2021 · 2021
Closest in time.
Adaptive Universal Generalized PageRank Graph Neural Network. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021 · 2021
Closest in time.
Very Deep Graph Neural Networks Via Noise Regularisation
Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, and Peter Battaglia. 2021 · 2021
Closest in time.
Knowledge-aware coupled graph neural network for social recommendation. In AAAI Conference on Artificial Intelligence (AAAI)
Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, and Yanfang Ye. 2021 · 2021
Closest in time.
Training Graph Neural Networks with 1000 Layers. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event . 6437–6449
Guohao Li, Matthias Müller, Bernard Ghanem, and Vladlen Koltun. 2021a · 2021
Closest in time.
Deepgcns: Making gcns go as deep as cnns
Guohao Li, Matthias Müller, Guocheng Qian, Itzel Carolina Delgadillo Perez, Abdulellah Abualshour, Ali Kassem Thabet, and Bernard Ghanem. 2021b · 2021
Closest in time.
OpenBox: A Generalized Black-box Optimization Service
Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu, Zhi Yang, et al · 2021
Closest in time.
Graph Neural Networks for Natural Language Processing: A Survey
Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long. 2021 · 2021
Closest in time.
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event . 11592–11602
Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, and Kenji Kawaguchi. 2021 · 2021
Closest in time.
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra. 2021 · 2021
Closest in time.
ROD: Reception-aware Online Distillation for Sparse Graphs
Wentao Zhang, Yuezihan Jiang, Yang Li, Zeang Sheng, Yu Shen, Xupeng Miao, Liang Wang, Zhi Yang, and Bin Cui. 2021a · 2021
Closest in time.
GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing
Wentao Zhang, Yu Shen, Zheyu Lin, Yang Li, Xiaosen Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui. 2021b · 2021
Closest in time.
Simple spectral graph convolution. In International Conference on Learning Representations
Hao Zhu and Piotr Koniusz. 2021 · 2021
Closest in time.