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Transformers have recently emerged as powerful neural networks for graph learning, showcasing state-of-the-art performance on several graph property prediction tasks.
The reduction of a graph to canonical form and the algebra which appears therein
Boris Weisfeiler and Andrei Leman · 1968
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Snap datasets: Stanford large network dataset collection, 2014
Jure Leskovec and Andrej Krevl · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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The extreme classification repository: Multi-label datasets and code. url: http://manikvarma. org/downloads/xc
K Bhatia, K Dahiya, H Jain, A Mittal, Y Prabhu, and M Varma · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Xavier Bresson and Thomas Laurent · 2017
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Stochastic training of graph convolutional networks with variance reduction
Jianfei Chen, Jun Zhu, and Le Song · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Adaptive sampling towards fast graph representation learning
Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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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
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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, et al · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
Cited alongside, same era.
Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
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
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Graph-less neural networks: Teaching old mlps new tricks via distillation
Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah · 2021
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Gophormer: Ego-graph transformer for node classification
Jianan Zhao, Chaozhuo Li, Qianlong Wen, Yiqi Wang, Yuming Liu, Hao Sun, Xing Xie, and Yanfang Ye · 2021
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Nagphormer: A tokenized graph transformer for node classification in large graphs
Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He · 2022
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Long range graph benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini · 2022
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Sign: Scalable inception graph neural networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, and Federico Monti · 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.
Combining label propagation and simple models out-performs graph neural networks
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin Benson · 2020
Cited alongside, same era.
Flag: Adversarial data augmentation for graph neural networks
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Cited alongside, same era.
Masked label prediction: Unified message passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun · 2020
Cited alongside, same era.
Microsoft academic graph: When experts are not enough
Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia · 2020
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Mlpinit: Embarrassingly simple gnn training acceleration with mlp initialization
Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, and Neil Shah · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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Friend story ranking with edge-contextual local graph convolutions
Xianfeng Tang, Yozen Liu, Xinran He, Suhang Wang, and Neil Shah · 2022
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Nodeformer: A scalable graph structure learning transformer for node classification
Qitian Wu, Wentao Zhao, Zenan Li, David P Wipf, and Junchi Yan · 2022
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Hierarchical graph transformer with adaptive node sampling
Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee · 2022
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On the connection between mpnn and graph transformer
Chen Cai, Truong Son Hy, Rose Yu, and Yusu Wang · 2023
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Linkless link prediction via relational distillation
Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V Chawla, Neil Shah, and Tong Zhao · 2023
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Goat: A global transformer on large-scale graphs
Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C Bayan Bruss, and Tom Goldstein · 2023
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Demystifying structural disparity in graph neural networks: Can one size fit all?
Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, and Jiliang Tang · 2023
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Attending to graph transformers
Luis Müller, Mikhail Galkin, Christopher Morris, and Ladislav Rampášek · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Embedding based retrieval in friend recommendation
Jiahui Shi, Vivek Chaurasiya, Yozen Liu, Shubham Vij, Yan Wu, Satya Kanduri, Neil Shah, Peicheng Yu, Nik Srivastava, Lei Shi, et al · 2023
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Exphormer: Sparse transformers for graphs
Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J Sutherland, and Ali Kemal Sinop · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Difformer: Scalable (graph) transformers induced by energy constrained diffusion
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, and Junchi Yan · 2023
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Hierarchical transformer for scalable graph learning
Wenhao Zhu, Tianyu Wen, Guojie Song, Xiaojun Ma, and Liang Wang · 2023
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