Fetching the paper…
Reading the bibliography…
Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods.
On the evolution of random graphs
P. Erdős and A Rényi · 1960
Earlier work this paper cites.
Some properties of line digraphs
Frank Harary and R. Z. Norman · 1960
Earlier work this paper cites.
Reduction of a graph to a canonical form and an algebra arising during this reduction
B. Yu. Weisfeiler and A. A. Leman · 1968
Earlier work this paper cites.
Graphs and hypergraphs
Claude Berge · 1973
Earlier work this paper cites.
Emergence of scaling in random networks
Albert-Laszlo Barabasi and Reka Albert · 1999
Earlier work this paper cites.
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 · 2005
Earlier work this paper cites.
Graphs over time: densification laws, shrinking diameters and possible explanations
Jure Leskovec, Jon M. Kleinberg, and Christos Faloutsos · 2005
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad · 2008
Earlier work this paper cites.
Fractional graph theory: a rational approach to the theory of graphs
Edward Scheinerman and Daniel Ullman · 2011
Earlier work this paper cites.
ZINC: A free tool to discover chemistry for biology
John J. Irwin, Teague Sterling, Michael M. Mysinger, Erin S. Bolstad, and Ryan G. Coleman · 2012
Earlier work this paper cites.
Generative adversarial networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Zinc 15 - ligand discovery for everyone
Teague Sterling and John Irwin · 2015
Earlier work this paper cites.
Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
Earlier work this paper cites.
Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
Earlier work this paper cites.
Pygsp: Graph signal processing in python, 10 2017
Michaël Defferrard, Lionel Martin, Rodrigo Pena, and Nathanaël Perraudin · 2017
Earlier work this paper cites.
The chembl database in 2017
Anna Gaulton, Anne Hersey, Michał Nowotka, A Patricia Bento, Jon Chambers, David Mendez, Prudence Mutowo, Francis Atkinson, Louisa J Bellis, Elena Cibrián-Uhalte, et al · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Classification in biological networks with hypergraphlet kernels
Jose Lugo-Martinez and Predrag Radivojac · 2017
Cited alongside, same era.
Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
Cited alongside, same era.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J. Smola · 2017
Cited alongside, same era.
Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
Cited alongside, same era.
Self-attention graph pooling
Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
Tight framelets on graphs for multiscale data analysis
Yu Guang Wang and Xiaosheng Zhuang · 2019
Later among the works it cites.
Haarpooling: Graph pooling with compressive haar basis
Yu Guang Wang, Ming Li, Zheng Ma, Guido Montúfar, Xiaosheng Zhuang, and Yanan Fan · 2019
Later among the works it cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
Cited alongside, same era.
Dual-primal graph convolutional networks
Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, and Michael M. Bronstein · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 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.
Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun · 2018
Cited alongside, same era.
Later among the works it cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Later among the works it cites.
Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
Later among the works it cites.
A fair comparison of graph neural networks for graph classification
Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli · 2020
Later among the works it cites.
A fair comparison of graph neural networks for graph classification
Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli · 2020
Later among the works it cites.
Graph random neural networks for semi-supervised learning on graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang · 2020
Later among the works it cites.
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec · 2020
Later among the works it cites.
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Vijay S. Pande Percy Liang, and Jure Leskovec · 2020
Later among the works it cites.
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
Later among the works it cites.
ASAP: adaptive structure aware pooling for learning hierarchical graph representations
Ekagra Ranjan, Soumya Sanyal, and Partha P. Talukdar · 2020
Later among the works it cites.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2020
Later among the works it cites.
Composition-based multi-relational graph convolutional networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Talukdar · 2020
Later among the works it cites.
NENN: incorporate node and edge features in graph neural networks
Yulei Yang and Dongsheng Li · 2020
Later among the works it cites.
Accurate learning of graph representations with graph multiset pooling
Jinheon Baek, Minki Kang, and Sung Ju Hwang · 2021
Closest in time.
Combining label propagation and simple models out-performs graph neural networks
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin Benson · 2021
Closest in time.
Mars: Markov molecular sampling for multi-objective drug discovery
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li · 2021
Closest in time.