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Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks.
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Reduction of a graph to a canonical form and an algebra arising during this reduction
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Multilayer feedforward networks are universal approximators
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LIBSVM: A library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
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ZINC: A free tool to discover chemistry for biology
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Adam: A method for stochastic optimization
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Quantum chemistry structures and properties of 134 kilo molecules
R. Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. A. von Lilienfeld · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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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
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2016
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Inductive representation learning on large graphs
William L. 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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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 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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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Deep sets
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Towards sparse hierarchical graph classifiers
Catalina Cangea, Petar Velickovic, Nikola Jovanovic, Thomas Kipf, and Pietro Liò · 2018
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Molgan: An implicit generative model for small molecular graphs
Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Unsupervised universal self-attention network for graph classification
Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung · 2019
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On the limitations of representing functions on sets
Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, and Michael A. Osborne · 2019
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Haarpooling: Graph pooling with compressive haar basis
Yu Guang Wang, Ming Li, Zheng Ma, Guido Montúfar, Xiaosheng Zhuang, and Yanan Fan · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2019
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Nicola De Cao and Thomas Kipf · 2018
Cited alongside, same era.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
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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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec · 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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Spectral clustering with graph neural networks for graph pooling
Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi · 2019
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Retrosynthesis prediction with conditional graph logic network
Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, and Le Song · 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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GSPN: generative shape proposal network for 3d instance segmentation in point cloud
Li Yi, Wang Zhao, He Wang, Minhyuk Sung, and Leonidas J. Guibas · 2019
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Position-aware graph neural networks
Jiaxuan You, Rex Ying, and Jure Leskovec · 2019
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun · 2019
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Memory-based graph networks
Amir Hosein Khas Ahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, and Quaid Morris · 2020
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Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
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A fair comparison of graph neural networks for graph classification
Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Rethinking pooling in graph neural networks
Diego P. P. Mesquita, Amauri H. Souza Jr., and Samuel Kaski · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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ASAP: adaptive structure aware pooling for learning hierarchical graph representations
Ekagra Ranjan, Soumya Sanyal, and Partha P. Talukdar · 2020
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GROVER: self-supervised message passing transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
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Structpool: Structured graph pooling via conditional random fields
Hao Yuan and Shuiwang Ji · 2020
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