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Many machine learning techniques have been proposed in the last few years to process data represented in graph-structured form.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity
Asim Kumar Debnath, Rosa L. Lopez de Compadre, Gargi Debnath, Alan J. Shusterman, and Corwin Hansch · 1991
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Signature verification using a "siamese" time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah · 1993
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Supervised neural networks for the classification of structures
Alessandro Sperduti and Antonina Starita · 1997
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On Graph Kernels: Hardness Results and Efficient Alternatives
Thomas Gartner, Peter Flach, Stefan Wrobel, and Thomas Gärtner · 2003
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Statistical evaluation of the predictive toxicology challenge 2000-2001
Hannu Toivonen, Ashwin Srinivasan, Ross D. King, Stefan Kramer, and Christoph Helma · 2003
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Shortest-Path Kernels on Graphs
K.M. Borgwardt and Hans-Peter Kriegel · 2005
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Reducing the Dimensionality of Data with Neural Networks
G. E. Hinton and Ruslan R Salakhutdinov · 2006
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Graph kernels based on tree patterns for molecules
Pierre Mahé and Jean-Philippe Vert · 2008
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Comparison of descriptor spaces for chemical compound retrieval and classification
Nikil Wale, Ian Watson, and George Karypis · 2008
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Learning nonsparse kernels by self-organizing maps for structured data
Fabio Aiolli, Giovanni Da San Martino, Markus Hagenbuchner, and Alessandro Sperduti · 2009
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Neural network for graphs: A contextual constructive approach
Alessio Micheli · 2009
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The Graph Neural Network Model
F. Scarselli, M. Gori, Ah Chung Ah Chung Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Efficient graphlet kernels for large graph comparison
Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt · 2009
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Fast neighborhood subgraph pairwise distance kernel
Fabrizio Costa and Kurt De Grave · 2010
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Why Does Unsupervised Pre-training Help Deep Learning ?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy (Google Research) Bengio · 2010
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
Cited alongside, same era.
Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
Cited alongside, same era.
Weisfeiler-Lehman Graph Kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt · 2011
Cited alongside, same era.
Learning discriminative fisher kernels
Laurens van der Maaten · 2011
Cited alongside, same era.
Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription
Nicolas Boulanger-Lewandowski, Yoshua Bengio, and Pascal Vincent · 2012
Cited alongside, same era.
A tree-based kernel for graphs
Giovanni Da San Martino, Nicolò Navarin, and Alessandro Sperduti · 2012
Cited alongside, same era.
Ordered Decompositional DAG Kernels Enhancements
Giovanni Da San Martino, Nicolò Navarin, and Alessandro Sperduti · 2016
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Gated Graph Sequence Neural Networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
Qianli Liao and Tomaso Poggio · 2016
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Tree-Based Kernel for Graphs With Continuous Attributes
Giovanni Da San Martino, Nicolò Navarin, and Alessandro Sperduti · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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A memory efficient graph kernel
Giovanni Da San Martino, Nicolò Navarin, and Alessandro Sperduti · 2012
Cited alongside, same era.
Acoustic Modeling Using Deep Belief Networks
Abdel-rahman Mohamed, George E. Dahl, and Geoffrey Hinton · 2012
Cited alongside, same era.
Efficient graph kernels by randomization
Marion Neumann, Novi Patricia, Roman Garnett, and Kristian Kersting · 2012
Cited alongside, same era.
Pre-training of Recurrent Neural Networks via Linear Autoencoders
Luca Pasa and Alessandro Sperduti · 2014
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams · 2015
Cited alongside, same era.
Propagation kernels: efficient graph kernels from propagated information
Marion Neumann, Roman Garnett, Christian Bauckhage, and Kristian Kersting · 2015
Cited alongside, same era.
William L. Hamilton, Rex 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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Deriving Neural Architectures from Sequence and Graph Kernels
Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
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Generative kernels for tree-structured data
Davide Bacciu, Alessio Micheli, and Alessandro Sperduti · 2018
Closest in time.
An Experimental Study of Neural Networks for Variable Graphs
Xavier Bresson and Thomas Laurent · 2018
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On Filter Size in Graph Convolutional Networks
Dinh V. Tran, Nicolò Navarin, and Alessandro Sperduti · 2018
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An End-to-End Deep Learning Architecture for Graph Classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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