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We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig · 2003
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Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
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Graph evolution: Densification and shrinking diameters
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos · 2007
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Slic superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, Sabine Süsstrunk, et al · 2012
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A new space for comparing graphs
Anshumali Shrivastava and Ping Li · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Matthew D Zeiler and Rob Fergus · 2014
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Adam: A method for stochastic optimization
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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Attentive explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Gaan: Gated attention networks for learning on large and spatiotemporal graphs
Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung · 2018
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Graph classification using structural attention
John Boaz Lee, Ryan Rossi, and Xiangnan Kong · 2018
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Attention models in graphs: A survey
John Boaz Lee, Ryan A Rossi, Sungchul Kim, Nesreen K Ahmed, and Eunyee Koh · 2018
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Spectralnet: Spectral clustering using deep neural networks
Uri Shaham, Kelly Stanton, Henry Li, Boaz Nadler, Ronen Basri, and Yuval Kluger · 2018
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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
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Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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A study and comparison of human and deep learning recognition performance under visual distortions
Samuel Dodge and Lina Karam · 2017
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Attention correctness in neural image captioning
Chenxi Liu, Junhua Mao, Fei Sha, and Alan L Yuille · 2017
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Attentive cross-modal paratope prediction
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Graph attention networks
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Hongyang Gao and Shuiwang Ji · 2018
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Splinecnn: Fast geometric deep learning with continuous b-spline kernels
Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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