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Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches.
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Mikhail Belkin, Partha Niyogi and Vikas Sindhwani · 2006
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“Semi-supervised text classification using EM”
Kamal Nigam, Andrew McCallum and Tom Mitchell · 2006
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“Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]”
Olivier Chapelle, Bernhard Scholkopf and Alexander Zien · 2009
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“Deep learning via semi-supervised embedding”
Jason Weston, Fr“’ed“’eric Ratle, Hossein Mobahi and Ronan Collobert · 2012
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“Spectral networks and locally connected networks on graphs”
Joan Bruna, Wojciech Zaremba, Arthur Szlam and Yann LeCun · 2013
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“Neural machine translation by jointly learning to align and translate”
Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio · 2014
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Alex Graves, Greg Wayne and Ivo Danihelka · 2014
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“Distributed representations of sentences and documents”
Q. Le and T. Mikolov · 2014
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“Deepwalk: Online learning of social representations”
B. Perozzi, R. Al-Rfou and S. Skiena · 2014
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“Convolutional networks on graphs for learning molecular fingerprints”
David Duvenaud et al · 2015
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“Deep convolutional networks on graph-structured data”
Mikael Henaff, Joan Bruna and Yann LeCun · 2015
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“Gated graph sequence neural networks”
Y. Li, D. Tarlow, M. Brockschmidt and R. Zemel · 2015
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“Line: Large-scale information network embedding”
“Graph Convolutional Matrix Completion”
R. Berg, T.. Kipf and M. Welling · 2017
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“Community Detection with Graph Neural Networks”
J. Bruna and X. Li · 2017
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“Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity”
Eliav Buchnik and Edith Cohen · 2017
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“Learning Combinatorial Optimization Algorithms over Graphs”
Hanjun Dai et al · 2017
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Yan Duan et al · 2017
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Jian Tang et al · 2015
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“Tensorflow: Large-scale machine learning on heterogeneous distributed systems”
Martin Abadi et al · 2016
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“Geometric deep learning: going beyond euclidean data”
Michael Bronstein et al · 2016
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“Discriminative embeddings of latent variable models for structured data”
H. Dai, B. Dai and L. Song · 2016
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“Convolutional neural networks on graphs with fast localized spectral filtering”
Micha“”el Defferrard, Xavier Bresson and Pierre Vandergheynst · 2016
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“node2vec: Scalable feature learning for networks”
A. Grover and J. Leskovec · 2016
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“Semi-supervised classification with graph convolutional networks”
T.. Kipf and M. Welling · 2016
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“LASAGNE: Locality And Structure Aware Graph Node Embedding”
Evgeniy Faerman, Felix Borutta, Kimon Fountoulakis and Michael Mahoney · 2017
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“Neural message passing for quantum chemistry”
Justin Gilmer et al · 2017
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“Inductive Representation Learning on Large Graphs”
W.. Hamilton, R. Ying and J. Leskovec · 2017
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“VAIN: Attentional Multi-agent Predictive Modeling”
Yedid Hoshen · 2017
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“A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks”
A. Nowak, S. Villar, A.. Bandeira and J. Bruna · 2017
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“Modeling Relational Data with Graph Convolutional Networks”
M. Schlichtkrull et al · 2017
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“Robust Spatial Filtering with Graph Convolutional Neural Networks”
Felipe Such et al · 2017
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“Dynamic Filters in Graph Convolutional Networks”
Nitika Verma, Edmond Boyer and Jakob Verbeek · 2017
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“Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection”
Xiaojun Xu et al · 2017
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“Fast network embedding enhancement via high order proximity approximation”
Cheng Yang, Maosong Sun, Zhiyuan Liu and Cunchao Tu · 2017
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“Show, attend and tell: Neural image caption generation with visual attention”
Kelvin Xu et al · 2057
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