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Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility of variational graph auto-encoders (VGAE) to model graph data.
Collective dynamics of small-world networks
Duncan J Watts and Steven H Strogatz · 1998
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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Variational relevance vector machines
Christopher M Bishop and Michael E Tipping · 2000
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Laplacian eigenmaps and spectral techniques for embedding and clustering
Mikhail Belkin and Partha Niyogi · 2002
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Measuring isp topologies with rocketfuel
Neil Spring, Ratul Mahajan, and David Wetherall · 2002
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Comparative assessment of large-scale data sets of protein–protein interactions
Christian Von Mering, Roland Krause, Berend Snel, Michael Cornell, Stephen G Oliver, Stanley Fields, and Peer Bork · 2002
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Link-based classification
Qing Lu and Lise Getoor · 2003
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Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
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Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani · 2006
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Finding community structure in networks using the eigenvectors of matrices
Mark EJ Newman · 2006
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Mixed membership stochastic blockmodels
Edoardo M Airoldi, David M Blei, Stephen E Fienberg, and Eric P Xing · 2008
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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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Graphical models, exponential families, and variational inference
Martin J Wainwright, Michael I Jordan, et al · 2008
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Leveraging social media networks for classification
Lei Tang and Huan Liu · 2011
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Deep learning via semi-supervised embedding
Jason Weston, Frederic Ratle, Hossein Mobahi, and Ronan Collobert · 2012
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Distributed large-scale natural graph factorization
Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexander J Smola · 2013
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martin Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Cited alongside, same era.
Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Fluid communities: a competitive, scalable and diverse community detection algorithm
Ferran Parés, Dario Garcia Gasulla, Armand Vilalta, Jonatan Moreno, Eduard Ayguadé, Jesús Labarta, Ulises Cortés, and Toyotaro Suzumura · 2017
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Gunnemann · 2018
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Danilo Jimenez Rezende and Shakir Mohamed · 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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Infinite edge partition models for overlapping community detection and link prediction
Mingyuan Zhou · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michael 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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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Auxiliary deep generative models
Lars Maaloe, Casper Kaae Sonderby, Soren Kaae Sonderby, and Ole Winther · 2016
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Harp: Hierarchical representation learning for networks
Haochen Chen, Bryan Perozzi, Yifan Hu, and Steven Skiena · 2018
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Hyperspherical variational auto-encoders
Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Semi-implicit variational inference
Mingzhang Yin and Mingyuan Zhou · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Robust negative sampling for network embedding
Mohammadreza Armandpour, Patrick Ding, Jianhua Huang, and Xia Hu · 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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