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Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.
A reduction of a graph to a canonical form and an algebra arising during this reduction
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Albert-László Barabási and Réka Albert · 1999
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The pagerank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd · 1999
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Nonlinear dimensionality reduction by locally linear embedding
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A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin De Silva, and John C Langford · 2000
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Birds of a feather: Homophily in social networks
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Statistical mechanics of complex networks
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
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Cauchy’s interlace theorem for eigenvalues of hermitian matrices
Suk-Geun Hwang · 2004
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Fast algorithms for approximate semidefinite programming using the multiplicative weights update method
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Graphs over time: densification laws, shrinking diameters and possible explanations
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos · 2005
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
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Yago: a core of semantic knowledge
Fabian M Suchanek, Gjergji Kasneci, and Gerhard Weikum · 2007
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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Wavelets on graphs via spectral graph theory
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval · 2011
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Rolx: structural role extraction & mining in large graphs
Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos, and Lei Li · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2014
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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Fast depth-based subgraph kernels for unattributed graphs
Lu Bai and Edwin R Hancock · 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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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Daan Wierstra, et al · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Giannis Nikolentzos, Giannis Siglidis, and Michalis Vazirgiannis · 2019
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang · 2019
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Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Lio, Yoshua Bengio, and R Devon Hjelm · 2019
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Stability and generalization of graph convolutional neural networks
Saurabh Verma and Zhi-Li Zhang · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Conditional structure generation through graph variational generative adversarial nets
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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struc2vec: Learning node representations from structural identity
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Easing embedding learning by comprehensive transcription of heterogeneous information networks
Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, and Jiawei Han · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, and Pan Li · 2019
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Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction
Jinheon Baek, Dong Bok Lee, and Sung Ju Hwang · 2020
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Graph kernels: State-of-the-art and future challenges
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Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
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Gpt-gnn: Generative pre-training of graph neural networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun · 2020
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A survey on graph kernels
Nils M Kriege, Fredrik D Johansson, and Christopher Morris · 2020
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Node classification on graphs with few-shot novel labels via meta transformed network embedding
Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, and Xiaohong Guan · 2020
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Graph neural networks exponentially lose expressive power for node classification
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Graph representation learning via graphical mutual information maximization
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Unsupervised domain adaptive graph convolutional networks
Man Wu, Shirui Pan, Chuan Zhou, Xiaojun Chang, and Xingquan Zhu · 2020
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Multisage: Empowering graphsage with contextualized multi-embedding on web-scale multipartite networks
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Heterogeneous network representation learning: A unified framework with survey and benchmark
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Co-embedding network nodes and hierarchical labels with taxonomy based generative adversarial nets
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Relation learning on social networks with multi-modal graph edge variational autoencoders
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Zero-shot scene graph relation prediction through commonsense knowledge integration
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Shift-robust gnns: Overcoming the limitations of localized graph training data
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