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Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved state-of-the-art performance in tasks such as node classification and link prediction.
Heterogeneous graph attention network
X. Wang, H. Ji, C. Shi, B. Wang, P. Cui, P. Yu, and Y. Ye · 1903
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The link-prediction problem for social networks
D. Liben-Nowell and J. Kleinberg · 2007
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Graph kernels
S. V. N. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt · 2010
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Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
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Deepwalk: Online learning of social representations
B. Perozzi, R. Al-Rfou, and S. Skiena · 2014
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Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Deep convolutional networks on graph-structured data
M. Henaff, J. Bruna, and Y. LeCun · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Line: Large-scale information network embedding
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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node2vec: Scalable feature learning for networks
A. Grover and J. Leskovec · 2016
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Variational graph auto-encoders
T. N. Kipf and M. Welling · 2016
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Geometric deep learning on graphs and manifolds using mixture model cnns
F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, and M. M. Bronstein · 2016
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A survey of heterogeneous information network analysis
C. Shi, Y. Li, J. Zhang, Y. Sun, and S. Y. Philip · 2016
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Structural deep network embedding
D. Wang, P. Cui, and W. Zhu · 2016
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Graph convolutional matrix completion
R. v. d. Berg, T. N. Kipf, and M. Welling · 2017
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Geometric deep learning: going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
Deriving neural architectures from sequence and graph kernels
T. Lei, W. Jin, R. Barzilay, and T. Jaakkola · 2017
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Geometric matrix completion with recurrent multi-graph neural networks
F. Monti, M. Bronstein, and X. Bresson · 2017
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Geniepath: Graph neural networks with adaptive receptive paths
Z. Liu, C. Chen, L. Li, J. Zhou, X. Li, L. Song, and Y. Qi · 2018
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Modeling relational data with graph convolutional networks
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling · 2018
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Link prediction based on graph neural networks
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Stochastic training of graph convolutional networks with variance reduction
J. Chen, J. Zhu, and L. Song · 2017
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metapath2vec: Scalable representation learning for heterogeneous networks
Y. Dong, N. V. Chawla, and A. Swami · 2017
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Distance metric learning using graph convolutional networks: Application to functional brain networks
S. I. Ktena, S. Parisot, E. Ferrante, M. Rajchl, M. C. H. Lee, B. Glocker, and D. Rueckert · 2017
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M. Zhang and Y. Chen · 2018
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Deep collective classification in heterogeneous information networks
Y. Zhang, Y. Xiong, X. Kong, S. Li, J. Mi, and Y. Zhu · 2018
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Hats: A hierarchical graph attention network for stock movement prediction, 2019
R. Kim, C. H. So, M. Jeong, S. Lee, J. Kim, and J. Kang · 2019
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Self-attention graph pooling
J. Lee, I. Lee, and J. Kang · 2019
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Heterogeneous graph attention networks for semi-supervised short text classification
H. Linmei, T. Yang, C. Shi, H. Ji, and X. Li · 2019
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Graph wavelet neural network
B. Xu, H. Shen, Q. Cao, Y. Qiu, and X. Cheng · 2019
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Heterogeneous graph neural network
C. Zhang, D. Song, C. Huang, A. Swami, and N. V. Chawla · 2019
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