Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data.
A new look at the power method for fast subspace tracking
Yingbo Hua, Yong Xiang, Tianping Chen, Karim Abed-Meraim, and Yongfeng Miao · 1999
Earlier work this paper cites.
Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2002
Earlier work this paper cites.
Sign: Scalable inception graph neural networks, 2020
Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, and Federico Monti · 2004
Earlier work this paper cites.
A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
Earlier work this paper cites.
Consistency of spectral clustering
Ulrike Von Luxburg, Mikhail Belkin, and Olivier Bousquet · 2008
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
Earlier work this paper cites.
On the power of combinatorial and spectral invariants
Martin Fürer · 2010
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
Earlier work this paper cites.
Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding
Vince Lyzinski, Daniel L Sussman, Minh Tang, Avanti Athreya, and Carey E Priebe · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Consistency of spectral clustering in stochastic block models
Jing Lei and Alessandro Rinaldo · 2015
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Inference via message passing on partially labeled stochastic block models
T Tony Cai, Tengyuan Liang, and Alexander Rakhlin · 2016
Earlier work this paper cites.
Statistical inference on random dot product graphs: a survey
Avanti Athreya, Donniell E Fishkind, Minh Tang, Carey E Priebe, Youngser Park, Joshua T Vogelstein, Keith Levin, Vince Lyzinski, and Yichen Qin · 2017
Earlier work this paper cites.
Community detection and stochastic block models: recent developments
Emmanuel Abbe · 2017
Earlier work this paper cites.
Hierarchical graph embedding in vector space by graph pyramid
Seyedeh Fatemeh Mousavi, Mehran Safayani, Abdolreza Mirzaei, and Hoda Bahonar · 2017
Earlier work this paper cites.
Supervised community detection with line graph neural networks
Zhengdao Chen, Xiang Li, and Joan Bruna · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Earlier work this paper cites.
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Q. Li, Z. Han, and X.-M. Wu · 2018
Earlier work this paper cites.
Graph attention networks, 2018
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Earlier work this paper cites.
Geniepath: Graph neural networks with adaptive receptive paths
Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, and Le Song · 2018
Earlier work this paper cites.
Diffusion scattering transforms on graphs
Fernando Gama, Alejandro Ribeiro, and Joan Bruna · 2018
Earlier work this paper cites.
Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
Earlier work this paper cites.
Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2018
Earlier work this paper cites.
Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Cited alongside, same era.
Graph neural networks exponentially lose expressive power for node classification
Kenta Oono and Taiji Suzuki · 2019
Cited alongside, same era.
Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
Cited alongside, same era.
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
Cited alongside, same era.
On the equivalence between graph isomorphism testing and function approximation with gnns
Zhengdao Chen, Soledad Villar, Lei Chen, and Joan Bruna · 2019
Cited alongside, same era.
On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2021
Later among the works it cites.
Understanding over-squashing and bottlenecks on graphs via curvature, 2021
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein · 2021
Later among the works it cites.
Graphnorm: A principled approach to accelerating graph neural network training
Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-yan Liu, and Liwei Wang · 2021
Later among the works it cites.
Do transformers really perform bad for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
Later among the works it cites.
Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Michael Perlmutter, Feng Gao, Guy Wolf, and Matthew Hirn · 2019
Cited alongside, same era.
Lanczosnet: Multi-scale deep graph convolutional networks
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard S Zemel · 2019
Cited alongside, same era.
Pairnorm: Tackling oversmoothing in gnns
Lingxiao Zhao and Leman Akoglu · 2019
Cited alongside, same era.
Position-aware graph neural networks
Jiaxuan You, Rex Ying, and Jure Leskovec · 2019
Cited alongside, same era.
Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks
Di Jin, Ziyang Liu, Weihao Li, Dongxiao He, and Weixiong Zhang · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Cited alongside, same era.
A short tutorial on the weisfeiler-lehman test and its variants
Ningyuan Huang and Soledad Villar · 2021
Later among the works it cites.
Weisfeiler and lehman go topological: Message passing simplicial networks
Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar, Pietro Lio, and Michael Bronstein · 2021
Later among the works it cites.
Reconstruction for powerful graph representations
Leonardo Cotta, Christopher Morris, and Bruno Ribeiro · 2021
Later among the works it cites.
Weisfeiler and leman go machine learning: The story so far, 2021
Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, and Karsten Borgwardt · 2021
Later among the works it cites.
Graph neural networks: Architectures, stability, and transferability
Luana Ruiz, Fernando Gama, and Alejandro Ribeiro · 2021
Later among the works it cites.
Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2021
Later among the works it cites.
Non-local graph neural networks
Meng Liu, Zhengyang Wang, and Shuiwang Ji · 2021
Later among the works it cites.
Representing long-range context for graph neural networks with global attention
Zhanghao Wu, Paras Jain, Matthew Wright, Azalia Mirhoseini, Joseph E Gonzalez, and Ion Stoica · 2021
Later among the works it cites.
A unified lottery ticket hypothesis for graph neural networks
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang · 2021
Later among the works it cites.
Performance-adaptive sampling strategy towards fast and accurate graph neural networks
Minji Yoon, Théophile Gervet, Baoxu Shi, Sufeng Niu, Qi He, and Jaewon Yang · 2021
Later among the works it cites.
One-hot graph encoder embedding, 2021
Cencheng Shen, Qizhe Wang, and Carey E. Priebe · 2021
Later among the works it cites.
Weisfeiler–leman and graph spectra, 2021
Gaurav Rattan and Tim Seppelt · 2021
Later among the works it cites.
Multi-scale attributed node embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar · 2021
Later among the works it cites.
Principled approach to the selection of the embedding dimension of networks
Weiwei Gu, Aditya Tandon, Yong-Yeol Ahn, and Filippo Radicchi · 2021
Later among the works it cites.
Is homophily a necessity for graph neural networks?
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang · 2022
Closest in time.
Learning graph normalization for graph neural networks
Yihao Chen, Xin Tang, Xianbiao Qi, Chun-Guang Li, and Rong Xiao · 2022
Closest in time.
Overcoming oversmoothness in graph convolutional networks via hybrid scattering networks
Frederik Wenkel, Yimeng Min, Matthew Hirn, Michael Perlmutter, and Guy Wolf · 2022
Closest in time.
Sign and basis invariant networks for spectral graph representation learning
Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, and Stefanie Jegelka · 2022
Closest in time.
Inferring from references with differences for semi-supervised node classification on graphs
Yi Luo, Guangchun Luo, Ke Yan, and Aiguo Chen · 2022
Closest in time.