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Graph representation learning is to learn universal node representations that preserve both node attributes and structural information.
Social network fusion and mining: a survey
Jiawei Zhang · 1911
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A mathematical theory of communication
Claude E Shannon · 1948
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Graph-based consensus maximization among multiple supervised and unsupervised models
Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, and Jiawei Han · 2009
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Pathsim: Meta path-based top-k similarity search in heterogeneous information networks
Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu · 2011
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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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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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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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Network representation learning with rich text information
Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Chang · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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Order matters: Sequence to sequence for sets
Manjunath Kudlur Oriol Vinyals, Samy Bengio · 2016
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Asymmetric transitivity preserving graph embedding
Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu · 2016
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Explaining reviews and ratings with paco: Poisson additive co-clustering
Chao-Yuan Wu, Alex Beutel, Amr Ahmed, and Alexander J Smola · 2016
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Collective classification via discriminative matrix factorization on sparsely labeled networks
Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang · 2016
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metapath2vec: Scalable representation learning for heterogeneous networks
Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami · 2017
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Learning graph representations with embedding propagation
Alberto Garcia Duran and Mathias Niepert · 2017
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Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning
Tao-yang Fu, Wang-Chien Lee, and Zhen Lei · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Representation learning on graphs: Methods and applications
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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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Adaptive sampling towards fast graph representation learning
Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 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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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Variational graph auto-encoders
T. N. Kipf and M. Welling · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Knowledge graph embedding: A survey of approaches and applications
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo · 2017
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Community preserving network embedding
Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang · 2017
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Mine: mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and R Devon Hjelm · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Ensemfdet: An ensemble approach to fraud detection based on bipartite graph
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Ke Sun, Zhouchen Lin, and Zhanxing Zhu · 2019
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Deep graph infomax
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Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu · 2019
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Shne: Representation learning for semantic-associated heterogeneous networks
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Hgat: Hierarchical graph attention network for fake news detection
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