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Graph transformers need strong inductive biases to derive meaningful attention scores.
A note on two problems in connexion with graphs
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Snap datasets: Stanford large network dataset collection. 2014
Jure Leskovec and Andrej Krevl · 2016
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Xavier Bresson and Thomas Laurent · 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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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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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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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 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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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Attpool: Towards hierarchical feature representation in graph convolutional networks via attention mechanism
Jingjia Huang, Zhangheng Li, Nannan Li, Shan Liu, and Ge Li · 2019
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Self-attention graph pooling
Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
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Graph reduction with spectral and cut guarantees
Andreas Loukas · 2019
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2019
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, 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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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2020
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Spectral clustering with graph neural networks for graph pooling
Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi · 2020
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Adaptive universal generalized pagerank graph neural network
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic · 2020
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Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 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
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
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Global self-attention as a replacement for graph convolution
Md Shamim Hussain, Mohammed J Zaki, and Dharmashankar Subramanian · 2022
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An analysis of virtual nodes in graph neural networks for link prediction (extended abstract)
EunJeong Hwang, Veronika Thost, Shib Sankar Dasgupta, and Tengfei Ma · 2022
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Transformer for graphs: An overview from architecture perspective
Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, and Yu Rong · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 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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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
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Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Asap: Adaptive structure aware pooling for learning hierarchical graph representations
Ekagra Ranjan, Soumya Sanyal, and Partha Talukdar · 2020
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Sign: Scalable inception graph neural networks
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael Bronstein, and Federico Monti · 2020
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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Equivariant subgraph aggregation networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M Bronstein, and Haggai Maron · 2021
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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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Specformer: Spectral graph neural networks meet transformers
Deyu Bo, Chuan Shi, Lele Wang, and Renjie Liao · 2023
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Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2023
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Graph transformers for large graphs
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Drew: Dynamically rewired message passing with delay
Benjamin Gutteridge, Xiaowen Dong, Michael M Bronstein, and Francesco Di Giovanni · 2023
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GOAT: A global transformer on large-scale graphs
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What improves the generalization of graph transformer? a theoretical dive into self-attention and positional encoding
Hongkang Li, Meng Wang, Tengfei Ma, Sijia Liu, ZAIXI ZHANG, and Pin-Yu Chen · 2023
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Gapformer: Graph transformer with graph pooling for node classification
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Graph inductive biases in transformers without message passing
Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K Dokania, Mark Coates, Philip Torr, and Ser-Nam Lim · 2023
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Rethinking structural encodings: Adaptive graph transformer for node classification task
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Multiresolution graph transformers and wavelet positional encoding for learning long-range and hierarchical structures
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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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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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Simplifying and empowering transformers for large-graph representations
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Rethinking the expressive power of GNNs via graph biconnectivity
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Hierarchical message-passing graph neural networks
Zhiqiang Zhong, Cheng-Te Li, and Jun Pang · 2023
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Hierarchical transformer for scalable graph learning
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Polynormer: Polynomial-expressive graph transformer in linear time
Chenhui Deng, Zichao Yue, and Zhiru Zhang · 2024
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VCR-graphormer: A mini-batch graph transformer via virtual connections
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Less is more: on the over-globalizing problem in graph transformers
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