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Several recent works use positional encodings to extend the receptive fields of graph neural network (GNN) layers equipped with attention mechanisms.
Spektren endlicher grafen
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Maximum likelihood from incomplete data via the em algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin · 1977
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Stable and informative spectral signatures for graph matching
Nan Hu, Raif M. Rustamov, and Leonidas Guibas · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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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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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Residual gated graph convnets, 2018
Xavier Bresson and Thomas Laurent · 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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Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
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Graph attention networks, 2018
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 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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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing, 2019
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan · 2019
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Graph transformer for graph-to-sequence learning
Deng Cai and Wai Lam · 2019
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Path-augmented graph transformer network, 2019
Benson Chen, Regina Barzilay, and Tommi Jaakkola · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc V. Le, and Ruslan Salakhutdinov · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Diffusion Improves Graph Learning
Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann · 2019
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Break the ceiling: Stronger multi-scale deep graph convolutional networks
Sitao Luan, Mingde Zhao, Xiao-Wen Chang, and Doina Precup · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Spagan: Shortest path graph attention network
Yiding Yang, Xinchao Wang, Mingli Song, Junsong Yuan, and Dacheng Tao · 2019
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Position-aware graph neural networks, 2019
Jiaxuan You, Rex Ying, and Jure Leskovec · 2019
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Graph transformer networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2019
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Bayesian graph convolutional neural networks for semi-supervised classification, 2018
Yingxue Zhang, Soumyasundar Pal, Mark Coates, and Deniz Üstebay · 2020
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2021
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Accurate learning of graph representations with graph multiset pooling, 2021
Jinheon Baek, Minki Kang, and Sung Ju Hwang · 2021
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On graph neural networks versus graph-augmented {mlp}s
Lei Chen, Zhengdao Chen, and Joan Bruna · 2021
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A generalization of transformer networks to graphs, 2021
Vijay Prakash Dwivedi and Xavier Bresson · 2021
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Mesh-based graph convolutional neural networks for modeling materials with microstructure, 2021
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The logical expressiveness of graph neural networks
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Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M. Bronstein · 2020
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Universal function approximation on graphs
Rickard Brüel Gabrielsson · 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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Benchmarking graph neural networks, 2020
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
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Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun · 2020
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Ari Frankel, Cosmin Safta, Coleman Alleman, and Reese Jones · 2021
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Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
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Is homophily a necessity for graph neural networks?, 2021
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang · 2021
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Scalable and adaptive graph neural networks with self-label-enhanced training, 2021
Chuxiong Sun, Hongming Gu, and Jie Hu · 2021
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein · 2021
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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
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