Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.
The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 1968
Earlier work this paper cites.
Practical protein crystallography (2nd Edition)
Duncan E McRee · 1999
Earlier work this paper cites.
Real-time volume graphics
Klaus Engel, Markus Hadwiger, Joe M Kniss, Aaron E Lefohn, Christof Rezk Salama, and Daniel Weiskopf · 2004
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.
The PASCAL visual object classes (VOC) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
Earlier work this paper cites.
SLIC Superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk · 2012
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.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko · 2013
Earlier work this paper cites.
Scikit-image: Image processing in python
François Boulogne, Joshua D Warner, and Emmanuelle Neil Yager · 2014
Earlier work this paper cites.
Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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.
SATPdb: a database of structurally annotated therapeutic peptides
Sandeep Singh, Kumardeep Chaudhary, Sandeep Kumar Dhanda, Sherry Bhalla, Salman Sadullah Usmani, Ankur Gautam, Abhishek Tuknait, Piyush Agrawal, Deepika Mathur, and Gajendra PS Raghava · 2016
Earlier work this paper cites.
Xavier Bresson and Thomas Laurent · 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.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Stochastic training of graph convolutional networks with variance reduction
Jianfei Chen, Jun Zhu, and Le Song · 2018
Earlier work this paper cites.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
Earlier work this paper cites.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Earlier work this paper cites.
Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
Cited alongside, same era.
Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
Cited alongside, same era.
Implicit graph neural networks
Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, and Laurent El Ghaoui · 2020
Cited alongside, same era.
Graph representation learning
William L. Hamilton · 2020
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
Deep learning on graphs
Yao Ma and Jiliang Tang · 2021
Later among the works it cites.
GraphiT: Encoding graph structure in transformers
Grégoire Mialon, Dexiong Chen, Margot Selosse, and Julien Mairal · 2021
Later among the works it cites.
Hierarchical graph neural nets can capture long-range interactions
Ladislav Rampášek and Guy Wolf · 2021
Later among the works it cites.
Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
Later among the works it cites.
Learning neural generative dynamics for molecular conformation generation
Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, and Jian Tang · 2021
Later among the works it cites.
Graph neural networks inspired by classical iterative algorithms
Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, and David Wipf · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Distance encoding: Design provably more powerful neural networks for graph representation learning
Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec · 2020
Cited alongside, same era.
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Christopher Morris, Gaurav Rattan, and Petra Mutzel · 2020
Cited alongside, same era.
Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
Cited alongside, same era.
Graph networks with spectral message passing
Kimberly Stachenfeld, Jonathan Godwin, and Peter Battaglia · 2020
Cited alongside, same era.
Long range arena: A benchmark for efficient transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler · 2020
Cited alongside, same era.
Design space for graph neural networks
Jiaxuan You, Rex Ying, and Jure Leskovec · 2020
Cited alongside, same era.
Later among the works it cites.
Do transformers really perform badly 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.
Do transformers really perform badly 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.
Nested graph neural networks
Muhan Zhang and Pan Li · 2021
Later among the works it cites.
Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos P Zafeiriou, and Michael Bronstein · 2022
Closest in time.
Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2022
Closest in time.
Zenodo repository of LRGB: Long Range Graph Benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini · 2022
Closest in time.
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
Charles Tapley Hoyt, Max Berrendorf, Mikhail Gaklin, Volker Tresp, and Benjamin M. Gyori · 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.
Mgnni: Multiscale graph neural networks with implicit layers
Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, and Xiaokui Xiao · 2022
Closest in time.
Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang · 2022
Closest in time.
Learn Science at Scitable: Peptide
Nature.com · 2022
Closest in time.
Visual object classes challenge 2011 (voc2011)
PascalVOC · 2022
Closest in time.
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
Closest in time.
Benchmarking graphormer on large-scale molecular modeling datasets
Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, and Tie-Yan Liu · 2022
Closest in time.
3d infomax improves gnns for molecular property prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò · 2022
Closest in time.
Graph neural networks
Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, and Le Song · 2022
Closest in time.
Where did the gap go? reassessing the long-range graph benchmark
Jan Tönshoff, Martin Ritzert, Eran Rosenbluth, and Martin Grohe · 2023
Closest in time.