Fetching the paper…
Reading the bibliography…
Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data.
Spectral graph theory
Fan RK Chung and Fan Chung Graham · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Link-based classification
Qing Lu and Lise Getoor · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
Earlier work this paper cites.
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Wavelets on graphs via spectral graph theory
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval · 2011
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
Cited alongside, same era.
Deep learning via semi-supervised embedding
Jason Weston, Frédéric Ratle, Hossein Mobahi, and Ronan Collobert · 2012
Cited alongside, same era.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst · 2013
Cited alongside, same era.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann Lecun · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
Later among the works it cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Later among the works it cites.
Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Cited alongside, same era.
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Later among the works it cites.
Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
Later among the works it cites.
Graph wavelet neural network
Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, and Xueqi Cheng · 2019
Later among the works it cites.