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Deep learning models for graphs have advanced the state of the art on many tasks.
On the optimization of a synaptic learning rule
Samy Bengio, Yoshua Bengio, Jocelyn Cloutier, and Jan Gecsei · 1992
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Meta-neural networks that learn by learning
Devang K Naik and RJ Mammone · 1992
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Learning to control fast-weight memories: An alternative to dynamic recurrent networks
Jürgen Schmidhuber · 1992
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Learning to learn: Introduction and overview
Sebastian Thrun and Lorien Pratt · 1998
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Gradient-based optimization of hyperparameters
Yoshua Bengio · 2000
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Automating the construction of internet portals with machine learning
Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore · 2000
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The political blogosphere and the 2004 US election: divided they blog
Lada A Adamic and Natalie Glance · 2005
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Semi-Supervised Learning. Adaptive Computation and Machine Learning series
Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien · 2006
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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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Convex adversarial collective classification
Mohamad Ali Torkamani and Daniel Lowd · 2013
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Learning activation functions to improve deep neural networks
Forest Agostinelli, Matthew Hoffman, Peter Sadowski, and Pierre Baldi · 2014
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Collective classification of network data
Ben London and Lise Getoor · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Cited alongside, same era.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Google Inc, Joan Bruna, Dumitru Erhan, Google Inc, Ian Goodfellow, and Rob Fergus · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Cited alongside, same era.
Using machine teaching to identify optimal training-set attacks on machine learners
Towards poisoning of deep learning algorithms with back-gradient optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C Lupu, and Fabio Roli · 2017
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Column networks for collective classification
Trang Pham, Truyen Tran, Dinh Q. Phung, and Svetha Venkatesh · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure
Aleksandar Bojchevski and Stephan Günnemann · 2018
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NetGAN: Generating graphs via random walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, and Stephan Günnemann · 2018
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Shike Mei and Xiaojin Zhu · 2015
Cited alongside, same era.
Practical attacks against graph-based clustering
Yizheng Chen, Yacin Nadji, Athanasios Kountouras, Fabian Monrose, Roberto Perdisci, Manos Antonakakis, and Nikolaos Vasiloglou · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Cited alongside, same era.
Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
Cited alongside, same era.
Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann
Cited in the paper.
Adversarial attacks on node embeddings
Aleksandar Bojchevski and Stephan Günnemann
Cited in the paper.
Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
Later among the works it cites.
On first-order meta-learning algorithms
Alex Nichol and John Schulman · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
Later among the works it cites.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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