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Node embeddings have become an ubiquitous technique for representing graph data in a low dimensional space.
Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
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Automatic multimedia cross-modal correlation discovery
Jia-Yu Pan, Hyung-Jeong Yang, Christos Faloutsos, and Pinar Duygulu · 2004
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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It’s who you know: graph mining using recursive structural features
Keith Henderson, Brian Gallagher, Lei Li, Leman Akoglu, Tina Eliassi-Rad, Hanghang Tong, and Christos Faloutsos · 2011
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Leveraging social media networks for classification
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Mgae: Marginalized graph autoencoder for graph clustering
Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang · 2017
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Predictive network representation learning for link prediction
Zhitao Wang, Chengyao Chen, and Wenjie Li · 2017
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A survey on network embedding
Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu · 2018
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Palash Goyal, Homa Hosseinmardi, Emilio Ferrara, and Aram Galstyan · 2018
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Adversarially regularized graph autoencoder
Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang · 2018
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