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Graph neural networks (GNNs) are shown to be successful in modeling applications with graph structures.
The centrality index of a graph
Gert Sabidussi · 1966
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A set of measures of centrality based on betweenness
Linton C Freeman · 1977
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Stochastic blockmodels: First steps
Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt · 1983
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Power and centrality: A family of measures
Phillip Bonacich · 1987
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Power and centrality: A family of measures
Phillip Bonacich · 1987
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Collective dynamics of ‘small-world’ networks
Duncan J. Watts and Steven H. Strogatz · 1998
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An o(m) algorithm for cores decomposition of networks
Vladimir Batagelj and Matjaz Zaversnik · 2003
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Network biology: understanding the cell’s functional organization
Albert-Laszlo Barabasi and Zoltan N Oltvai · 2004
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Learning to rank using gradient descent
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender · 2005
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Subgraph centrality in complex networks
Ernesto Estrada and Juan A Rodriguez-Velazquez · 2005
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Graph clustering
Satu Elisa Schaeffer · 2007
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Exploring network structure, dynamics, and function using networkx
Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart · 2008
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Complex brain networks: graph theoretical analysis of structural and functional systems
Ed Bullmore and Olaf Sporns · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Networks: an introduction
Mark Newman · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Reasoning with neural tensor networks for knowledge base completion
Richard Socher, Danqi Chen, Christopher D. Manning, and Andrew Y. Ng · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Distributed representations of sentences and documents
Quoc Le and Tomas Mikolov · 2014
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SNAP Datasets: Stanford large network dataset collection
Jure Leskovec and Andrej Krevl · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Influence maximization in complex networks through optimal percolation
Flaviano Morone and Hernán A. Makse · 2015
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Modeling relational data with graph convolutional networks
Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec · 2018
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To tune or not to tune? adapting pretrained representations to diverse tasks
Matthew Peters, Sebastian Ruder, and Noah A. Smith · 2019
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Deep graph infomax
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm · 2019
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