Fetching the paper…
Reading the bibliography…
Message passing neural networks have recently evolved into a state-of-the-art approach to representation learning on graphs.
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd, ‘The pagerank citation ranking: Bringing order to the web.’, Technical report, Stanford InfoLab, (1999)
1999
Earlier work this paper cites.
Glen Jeh and Jennifer Widom, ‘Scaling personalized web search’, WWW
2003
Earlier work this paper cites.
Pavel Berkhin, ‘Bookmark-coloring algorithm for personalized pagerank computing’, Internet Mathematics
2006
Earlier work this paper cites.
Reid Andersen, Christian Borgs, Jennifer Chayes, John Hopcraft, Vahab S Mirrokni, and Shang-Hua Teng, ‘Local computation of pagerank contributions’, International Workshop on Algorithms and Models for the Web-Graph
2007
Earlier work this paper cites.
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad, ‘Collective classification in network data’, AI magazine
2008
Earlier work this paper cites.
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini, ‘The graph neural network model’, IEEE Transactions on Neural Networks
2009
Earlier work this paper cites.
Galileo Namata, Ben London, Lise Getoor, Bert Huang, and UMD EDU, ‘Query-driven active surveying for collective classification’, MLG
2012
Earlier work this paper cites.
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun, ‘Spectral networks and locally connected networks on graphs’, ICLR
2014
Earlier work this paper cites.
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena, ‘Deepwalk: Online learning of social representations’, SIGKDD
2014
Earlier work this paper cites.
Shaosheng Cao, Wei Lu, and Qiongkai Xu, ‘Grarep: Learning graph representations with global structural information’, CIKM
2015
Earlier work this paper cites.
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams, ‘Convolutional networks on graphs for learning molecular fingerprints’, NeurIPS
2015
Earlier work this paper cites.
Diederik P Kingma and Jimmy Ba, ‘Adam: A method for stochastic optimization’, ICLR
2015
Earlier work this paper cites.
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei, ‘Line: Large-scale information network embedding’, WWW
2015
Earlier work this paper cites.
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst, ‘Convolutional neural networks on graphs with fast localized spectral filtering’, NeurIPS
2016
Earlier work this paper cites.
Aditya Grover and Jure Leskovec, ‘node2vec: Scalable feature learning for networks’, SIGKDD
2016
Earlier work this paper cites.
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley, ‘Molecular graph convolutions: moving beyond fingerprints’, Journal of computer-aided molecular design
2016
Earlier work this paper cites.
Thomas N Kipf and Max Welling, ‘Variational graph auto-encoders’, NeurIPS Bayesian Deep Learning Workshop
2016
Cited alongside, same era.
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel, ‘Gated graph sequence neural networks’, ICLR
2016
Cited alongside, same era.
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov, ‘Learning convolutional neural networks for graphs’, ICML
2016
Cited alongside, same era.
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst, ‘Geometric deep learning: going beyond euclidean data’, IEEE Signal Processing Magazine
2017
Cited alongside, same era.
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl, ‘Neural message passing for quantum chemistry’, ICML
2017
Cited alongside, same era.
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling, ‘Modeling relational data with graph convolutional networks’, ESWC
2018
Later among the works it cites.
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann, ‘Pitfalls of graph neural network evaluation’, NeurIPS Relational Representation Learning Workshop
2018
Later among the works it cites.
2018
Later among the works it cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio, ‘Graph attention networks’, ICLR
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Will Hamilton, Zhitao Ying, and Jure Leskovec, ‘Inductive representation learning on large graphs’, NeurIPS
2017
Cited alongside, same era.
Thomas N Kipf and Max Welling, ‘Semi-supervised classification with graph convolutional networks’, ICLR
2017
Cited alongside, same era.
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein, ‘Geometric deep learning on graphs and manifolds using mixture model cnns’, CVPR
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Jie Chen, Tengfei Ma, and Cao Xiao, ‘Fastgcn: fast learning with graph convolutional networks via importance sampling’, ICLR
2018
Cited alongside, same era.
Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, and Le Song, ‘Learning steady-states of iterative algorithms over graphs’, ICML
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Later among the works it cites.
Muhan Zhang and Yixin Chen, ‘Link prediction based on graph neural networks’, NeurIPS
2018
Later among the works it cites.
Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Martin Blais, Amol Kapoor, Michal Lukasik, and Stephan Günnemann, ‘Is pagerank all you need for scalable graph neural networks?’, MLG
2019
Later among the works it cites.
Felix Borutta, Julian Busch, Evgeniy Faerman, Adina Klink, and Matthias Schubert, ‘Structural graph representations based on multiscale local network topologies’, WI
2019
Later among the works it cites.
2019
Later among the works it cites.
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann, ‘Predict then propagate: Graph neural networks meet personalized pagerank’, ICLR
2019
Later among the works it cites.
John Boaz Lee, Ryan A Rossi, Sungchul Kim, Nesreen K Ahmed, and Eunyee Koh, ‘Attention models in graphs: A survey’, TKDD
2019
Later among the works it cites.
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard S Zemel, ‘Lanczosnet: Multi-scale deep graph convolutional networks’, ICLR
2019
Later among the works it cites.
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe, ‘Weisfeiler and leman go neural: Higher-order graph neural networks’, AAAI
2019
Later among the works it cites.
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, and Kilian Q Weinberger, ‘Simplifying graph convolutional networks’, ICML
2019
Later among the works it cites.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka, ‘How powerful are graph neural networks?’, ICLR
2019
Later among the works it cites.