Fetching the paper…
Reading the bibliography…
Graphon is a nonparametric model that generates graphs with arbitrary sizes and can be induced from graphs easily.
On the evolution of random graphs
Paul Erdős and Alfréd Rényi · 1960
Earlier work this paper cites.
The fourier transform and its applications
Ron Bracewell · 1966
Earlier work this paper cites.
On graph kernels: Hardness results and efficient alternatives
Thomas Gärtner, Peter Flach, and Stefan Wrobel · 2003
Earlier work this paper cites.
Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
Earlier work this paper cites.
Convergent sequences of dense graphs I: Subgraph frequencies, metric properties and testing
Christian Borgs, Jennifer T Chayes, László Lovász, Vera T Sós, and Katalin Vesztergombi · 2008
Earlier work this paper cites.
Efficient graphlet kernels for large graph comparison
Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt · 2009
Earlier work this paper cites.
Matrix completion from a few entries
Raghunandan H Keshavan, Andrea Montanari, and Sewoong Oh · 2010
Earlier work this paper cites.
Graph classification and clustering based on vector space embedding
Kaspar Riesen and Horst Bunke · 2010
Earlier work this paper cites.
Libsvm: a library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
Earlier work this paper cites.
Extremal combinatorics: with applications in computer science
Stasys Jukna · 2011
Earlier work this paper cites.
Gromov-Wasserstein distances and the metric approach to object matching
Facundo Mémoli · 2011
Earlier work this paper cites.
Weisfeiler-Lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
Earlier work this paper cites.
Classification and estimation in the stochastic blockmodel based on the empirical degrees
Antoine Channarond, Jean-Jacques Daudin, Stéphane Robin, et al · 2012
Earlier work this paper cites.
Subgraph matching kernels for attributed graphs
Nils Kriege and Petra Mutzel · 2012
Earlier work this paper cites.
Large networks and graph limits
László Lovász · 2012
Earlier work this paper cites.
Stochastic blockmodel approximation of a graphon: theory and consistent estimation
Edo M Airoldi, Thiago B Costa, and Stanley H Chan · 2013
Earlier work this paper cites.
Graphons, cut norm and distance, couplings and rearrangements
Svante Janson · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
A consistent histogram estimator for exchangeable graph models
Stanley Chan and Edoardo Airoldi · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron 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.
Matrix estimation by universal singular value thresholding
Sourav Chatterjee et al · 2015
Earlier work this paper cites.
Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Cited alongside, same era.
Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
The multiscale Laplacian graph kernel
Risi Kondor and Horace Pan · 2016
Cited alongside, same era.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
Cited alongside, same era.
Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov · 2016
Cited alongside, same era.
Reward augmented maximum likelihood for neural structured prediction
Mohammad Norouzi, Samy Bengio, Navdeep Jaitly, Mike Schuster, Yonghui Wu, Dale Schuurmans, et al · 2016
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
Later among the works it cites.
Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec · 2018
Later among the works it cites.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
Later among the works it cites.
Graphon control of large-scale networks of linear systems
Shuang Gao and Peter E Caines · 2019
Later among the works it cites.
Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2019
Later among the works it cites.
InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gromov-Wasserstein averaging of kernel and distance matrices
Gabriel Peyré, Marco Cuturi, and Justin Solomon · 2016
Cited alongside, same era.
Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2016
Cited alongside, same era.
Structural deep network embedding
Daixin Wang, Peng Cui, and Wenwu Zhu · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Cited alongside, same era.
graph2vec: Learning distributed representations of graphs
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal · 2017
Cited alongside, same era.
Sub2vec: Feature learning for subgraphs
Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, and B Aditya Prakash · 2018
Cited alongside, same era.
Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang · 2019
Later among the works it cites.
Variational autoencoder with implicit optimal priors
Hiroshi Takahashi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada, and Satoshi Yagi · 2019
Later among the works it cites.
Optimal transport for structured data with application on graphs
Vayer Titouan, Nicolas Courty, Romain Tavenard, and Rémi Flamary · 2019
Later among the works it cites.
Topic-guided variational auto-encoder for text generation
Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, and Lawrence Carin · 2019
Later among the works it cites.
Scalable Gromov-Wasserstein learning for graph partitioning and matching
Hongteng Xu, Dixin Luo, and Lawrence Carin · 2019
Later among the works it cites.
Conditional structure generation through graph variational generative adversarial nets
Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, and Pan Li · 2019
Later among the works it cites.
Scalable deep generative modeling for sparse graphs
Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, and Dale Schuurmans · 2020
Later among the works it cites.
Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 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.
Graphon neural networks and the transferability of graph neural networks
Luana Ruiz, Luiz Chamon, and Alejandro Ribeiro · 2020
Later among the works it cites.
The graphon Fourier transform
Luana Ruiz, Luiz FO Chamon, and Alejandro Ribeiro · 2020
Later among the works it cites.
Fused Gromov-Wasserstein distance for structured objects
Vayer Titouan, Laetitia Chapel, Rémi Flamary, Romain Tavenard, and Nicolas Courty · 2020
Later among the works it cites.
Gromov-wasserstein factorization models for graph clustering
Hongteng Xu · 2020
Later among the works it cites.
Learning autoencoders with relational regularization
Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, and Lawrence Carin · 2020
Later among the works it cites.
Graphon filters: Graph signal processing in the limit
Matthew W Morency and Geert Leus · 2021
Closest in time.
Online graph dictionary learning
Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, and Nicolas Courty · 2021
Closest in time.
A hypergradient approach to robust regression without correspondence
Yujia Xie, Yixiu Mao, Simiao Zuo, Hongteng Xu, Xiaojing Ye, Tuo Zhao, and Hongyuan Zha · 2021
Closest in time.
Learning graphons via structured gromov-wasserstein barycenters
Hongteng Xu, Dixin Luo, Lawrence Carin, and Hongyuan Zha · 2021
Closest in time.