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The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community.
Random graphs
Edgar N Gilbert · 1959
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On the evolution of random graphs
Paul Erdős and Alfréd Rényi · 1960
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Graph theory with applications
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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The Cascade-Correlation learning architecture
Scott E. Fahlman and Christian Lebiere · 1990
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The self-organizing map
Teuvo Kohonen · 1990
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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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Untersuchungen zu dynamischen neuronalen netzen
Sepp Hochreiter · 1991
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Random walks on graphs: A survey
László Lovász and others · 1993
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Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
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Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
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Convolutional networks for images, speech, and time series
Yann LeCun, Yoshua Bengio, and others · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Supervised neural networks for the classification of structures
Alessandro Sperduti and Antonina Starita · 1997
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A general framework for adaptive processing of data structures
Paolo Frasconi, Marco Gori, and Alessandro Sperduti · 1998
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Mixed memory Markov models: Decomposing complex stochastic processes as mixtures of simpler ones
Lawrence K Saul and Michael I Jordan · 1999
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Application of cascade correlation networks for structures to chemistry
Anna Maria Bianucci, Alessio Micheli, Alessandro Sperduti, and Antonina Starita · 2000
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The elements of statistical learning
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
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The predictive toxicology challenge 2000–2001
Christoph Helma, Ross D. King, Stefan Kramer, and Ashwin Srinivasan · 2001
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Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig · 2003
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A self-organizing map for adaptive processing of structured data
Markus Hagenbuchner, Alessandro Sperduti, and Ah Chung Tsoi · 2003
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A general framework for unsupervised processing of structured data
Barbara Hammer, Alessio Micheli, Alessandro Sperduti, and Marc Strickert · 2004
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Recursive self-organizing network models
Barbara Hammer, Alessio Micheli, Alessandro Sperduti, and Marc Strickert · 2004
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Contextual processing of structured data by recursive cascade correlation
Alessio Micheli, Diego Sona, and Alessandro Sperduti · 2004
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BRENDA, the enzyme database: updates and major new developments
Ida Schomburg, Antje Chang, Christian Ebeling, Marion Gremse, Christian Heldt, Gregor Huhn, and Dietmar Schomburg · 2004
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Chapter 2 - 2d fourier theory
Jonathan M. Blackledge · 2005
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Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
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Universal approximation capability of cascade correlation for structures
Barbara Hammer, Alessio Micheli, and Alessandro Sperduti · 2005
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Self-organizing maps for learning the edit costs in graph matching
Michel Neuhaus and Horst Bunke · 2005
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Graph kernels for chemical informatics
Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre Baldi · 2005
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Semi-supervised learning
Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien · 2006
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Weighted graph cuts without eigenvectors a multilevel approach
Inderjit S Dhillon, Yuqiang Guan, and Brian Kulis · 2007
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Classification in networked data: A toolkit and a univariate case study
Sofus A Macskassy and Foster Provost · 2007
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An introduction to recursive neural networks and kernel methods for cheminformatics
Alessio Micheli, Alessandro Sperduti, and Antonina Starita · 2007
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A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
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Learning with hypergraphs: Clustering, classification, and embedding
Dengyong Zhou, Jiayuan Huang, and Bernhard Schölkopf · 2007
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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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Comparison of descriptor spaces for chemical compound retrieval and classification
Nikil Wale, Ian A Watson, and George Karypis · 2008
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Graph self-organizing maps for cyclic and unbounded graphs
Markus Hagenbuchner, Alessandro Sperduti, and Ah Chung Tsoi · 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
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele 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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A maximum-likelihood connectionist model for unsupervised learning over graphical domains
Edmondo Trentin and Leonardo Rigutini · 2009
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Graph echo state networks
Claudio Gallicchio and Alessio Micheli · 2010
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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Wavelets on graphs via spectral graph theory
David K Hammond, Pierre Vandergheynst, and Rémi Gribonval · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Parsing natural scenes and natural language with recursive neural networks
Richard Socher, Cliff C Lin, Chris Manning, and Andrew Y Ng · 2011
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Compositional generative mapping for tree-structured data - part I: Bottom-up probabilistic modeling of trees
Davide Bacciu, Alessio Micheli, and Alessandro Sperduti · 2012
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Query-driven active surveying for collective classification
Galileo Mark Namata, Ben London, Lise Getoor, and Bert Huang · 2012
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Recommender systems survey
Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez · 2013
Cited alongside, same era.
Rectifier nonlinearities improve neural network acoustic models
Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng · 2013
Cited alongside, same era.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
Cited alongside, same era.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Cited alongside, same era.
klog: A language for logical and relational learning with kernels
Paolo Frasconi, Fabrizio Costa, Luc De Raedt, and Kurt De Grave · 2014
Cited alongside, same era.
Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
GraphVAE: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
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Nonparametric small random networks for graph-structured pattern recognition
Edmondo Trentin and Ernesto Di Iorio · 2018
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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GraphGAN: Graph representation learning with generative adversarial nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo · 2018
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Videos as space-time region graphs
Xiaolong Wang and Abhinav Gupta · 2018
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Cited alongside, same era.
Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
Cited alongside, same era.
Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarelli, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P. Adams · 2015
Cited alongside, same era.
Improved semantic representations from tree-structured Long Short-Term Memory networks
Kai Sheng Tai, Richard Socher, and Christopher D. Manning · 2015
Cited alongside, same era.
Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec · 2018
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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
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GraphRNN: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, and Jure Leskovec · 2018
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu · 2018
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Concept drift and anomaly detection in graph streams
Daniele Zambon, Cesare Alippi, and Lorenzo Livi · 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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Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2018
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Dynamic hypergraph structure learning
Zizhao Zhang, Haojie Lin, Yue Gao, and KLISS BNRist · 2018
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Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec · 2018
Later among the works it cites.
Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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A non-negative factorization approach to node pooling in graph convolutional neural networks
Davide Bacciu and Luigi Di Sotto · 2019
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Edge-based sequential graph generation with recurrent neural networks
Davide Bacciu, Alessio Micheli, and Marco Podda · 2019
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Graph generation by sequential edge prediction
Davide Bacciu, Alessio Micheli, and Marco Podda · 2019
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A model to search for synthesizable molecules
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, and José Miguel Hernández-Lobato · 2019
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Conditional labeled graph generation with GANs
S. Fan and B. Huang · 2019
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Graph adversarial training: Dynamically regularizing based on graph structure
Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua · 2019
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Hypergraph neural networks
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Towards graph pooling by edge contraction
Michael Truong Le Frederik Diehl, Thomas Brunner and Alois Knoll · 2019
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Graph U-nets
Hongyang Gao and Shuiwang Ji · 2019
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Graphite: Iterative generative modeling of graphs
Aditya Grover, Aaron Zweig, and Stefano Ermon · 2019
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FP2VEC: A new molecular featurizer for learning molecular properties
Woosung Jeon and Dongsup Kim · 2019
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Dynamic hypergraph neural networks
Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, and Yue Gao · 2019
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Latent adversarial training of graph convolution networks
H. Jin and X. Zhang · 2019
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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation
Youngchun Kwon, Jiho Yoo, Youn-Suk Choi, Won-Joon Son, Dongseon Lee, and Seokho Kang · 2019
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Self-attention graph pooling
Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
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Safe: Self-attentive function embeddings for binary similarity
Luca Massarelli, Giuseppe Antonio Di Luna, Fabio Petroni, Roberto Baldoni, and Leonardo Querzoni · 2019
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GMNN: Graph Markov Neural Networks
Meng Qu, Yoshua Bengio, and Jian Tang · 2019
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NeVAE: A deep generative model for molecular graphs
Bidisha Samanta, Abir De, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez Rodriguez · 2019
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Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
Tae San Kim, Won Kyung Lee, and So Young Sohn · 2019
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Deep Graph Infomax
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm · 2019
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On the limitations of representing functions on sets
Edward Wagstaff, Fabian B Fuchs, Martin Engelcke, Ingmar Posner, and Michael Osborne · 2019
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Deep Graph Library: Towards efficient and scalable deep learning on graphs
Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J Smola, and Zheng Zhang · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 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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How powerful are graph neural networks?
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Topology optimization based graph convolutional network
Liang Yang, Zesheng Kang, Xiaochun Cao, Di Jin, Bo Yang, and Yuanfang Guo · 2019
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A deeper graph neural network for recommender systems
Ruiping Yin, Kan Li, Guangquan Zhang, and Jie Lu · 2019
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Graph convolutional networks: a comprehensive review
Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski · 2019
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Deep tree transductions - a short survey
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A fair comparison of graph neural networks for graph classification
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Fast and deep graph neural networks
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