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The emerging graph Transformers have achieved impressive performance for graph representation learning over graph neural networks (GNNs).
Protein Function Prediction via Graph Kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel. 2005 · 2005
Earlier work this paper cites.
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 2005 . 177–187
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2005 · 2005
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey E. Hinton. 2008 · 2008
Earlier work this paper cites.
Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification
Nikil Wale, Ian A Watson, and George Karypis. 2008 · 2008
Earlier work this paper cites.
Graph Invariant Kernels. In Proceedings of international joint conference on artificial intelligence , Vol. 2015. 3756–3762
Francesco Orsini, Paolo Frasconi, and Luc De Raedt. 2015 · 2015
Earlier work this paper cites.
Order Matters: Sequence to Sequence for Sets. In Proceedings of the International Conference on Learning Representations, 2016
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur. 2016 · 2016
Earlier work this paper cites.
Neural Message Passing for Quantum Chemistry. In Proceedings of the International Conference on Machine Learning , Vol. 70. 1263–1272
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs. In Proceedings of the Advances in Neural Information Processing Systems, 2017
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Semi-supervised Classification with Graph Convolutional Networks. In Proceedings of the International Conference on Learning Representations
Thomas N Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Attention Is All You Need. In Proceedings of the Advances in Neural Information Processing Systems , Vol. 30. 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Graph Attention Networks. In Proceedings of the International Conference on Learning Representations
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Representation Learning on Graphs with Jumping Knowledge Networks. In Proceedings of the International Conference on Machine Learning , Vol. 80. 5449–5458
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018 · 2018
Earlier work this paper cites.
An End-to-End Deep Learning Architecture for Graph Classification. In Proceedings of the AAAI Conference on Artificial Intelligence, 2018 , Vol. 32
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. 2018 · 2018
Earlier work this paper cites.
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. In Proceedings of the International Conference on Machine Learning , Vol. 97. 21–29
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019 · 2019
Earlier work this paper cites.
Signed Graph Attention Networks. In Proceedings of the International Conference on Artificial Neural Networks , Vol. 11731. 566–577
Junjie Huang, Huawei Shen, Liang Hou, and Xueqi Cheng. 2019 · 2019
Earlier work this paper cites.
Self-Attention Graph Pooling. In Proceedings of International Conference on Machine Learning, 2019 . 3734–3743
Junhyun Lee, Inyeop Lee, and Jaewoo Kang. 2019 · 2019
Earlier work this paper cites.
Decoupled Weight Decay Regularization. In Proceedings of the International Conference on Learning Representations
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Cited alongside, same era.
Simplifying Graph Convolutional Networks. In Proceedings of the International Conference on Machine Learning . 6861–6871
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019 · 2019
Cited alongside, same era.
How Powerful are Graph Neural Networks?. In Proceedings of the International Conference on Learning Representations
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Cited alongside, same era.
A Generalization of Transformer Networks to Graphs
Vijay Prakash Dwivedi and Xavier Bresson. 2020 · 2020
Cited alongside, same era.
Learning Signed Network Embedding via Graph Attention. In Proceedings of the AAAI Conference on Artificial Intelligence . 4772–4779
Node Similarity Preserving Graph Convolutional Networks. In Proceedings of the ACM International Conference on Web Search and Data Mining . 148–156
Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, and Jiliang Tang. 2021 · 2021
Later among the works it cites.
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision. In Proceedings of the International Conference on Learning Representations
Dongkwan Kim and Alice Oh. 2021 · 2021
Later among the works it cites.
Rethinking Graph Transformers with Spectral Attention. In Proceedings of the Advances in Neural Information Processing Systems , Vol. 34. 21618–21629
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou. 2021 · 2021
Later among the works it cites.
Do Transformers Really Perform Badly for Graph Representation. In Proceedings of the Advances in Neural Information Processing Systems , Vol. 34. 28877–28888
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021 · 2021
Later among the works it cites.
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Yu Li, Yuan Tian, Jiawei Zhang, and Yi Chang. 2020 · 2020
Cited alongside, same era.
Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. 2020 · 2020
Cited alongside, same era.
QSAN: A Quantum-probability based Signed Attention Network for Explainable False Information Detection. In Proceedings of the ACM International Conference on Information and Knowledge Management . 1445–1454
Tian Tian, Yudong Liu, Xiaoyu Yang, Yuefei Lyu, Xi Zhang, and Binxing Fang. 2020 · 2020
Cited alongside, same era.
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1243–1253
Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, and Jian Pei. 2020 · 2020
Cited alongside, same era.
On the Bottleneck of Graph Neural Networks and its Practical Implications. In Proceedings of the International Conference on Learning Representations
Uri Alon and Eran Yahav. 2021 · 2021
Cited alongside, same era.
Beyond Low-frequency Information in Graph Convolutional Networks. In Proceedings of the AAAI Conference on Artificial Intelligence . 3950–3957
Deyu Bo, Xiao Wang, Chuan Shi, and Huawei Shen. 2021 · 2021
Cited alongside, same era.
Adaptive Universal Generalized PageRank Graph Neural Network. In Proceedings of the International Conference on Learning Representations
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021 · 2021
Cited alongside, same era.
Rethinking Attention with Performers. In Proceedings of the International Conference on Learning Representations
Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamás Sarlós, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J. Colwell, and Adrian Weller. 2021 · 2021
Cited alongside, same era.
Jianan Zhao, Chaozhuo Li, Qianlong Wen, Yiqi Wang, Yuming Liu, Hao Sun, Xing Xie, and Yanfang Ye. 2021 · 2021
Later among the works it cites.
How Expressive are Transformers in Spectral Domain for Graphs?
Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Hiroki Kanezashi, Toyotaro Suzumura, and Isaiah Onando Mulang’. 2022 · 2022
Later among the works it cites.
Structure-aware Transformer for Graph Representation Learning. In International Conference on Machine Learning , Vol. 162. 3469–3489
Dexiong Chen, Leslie O’Bray, and Karsten Borgwardt. 2022 · 2022
Later among the works it cites.
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily. In Proceedings of the International Conference on Machine Learning , Vol. 162. 13242–13256
Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian. 2022 · 2022
Later among the works it cites.
Recipe for a General, Powerful, Scalable Graph Transformer. In Proceedings of the Advances in Neural Information Processing Systems , Vol. 35. 14501–14515
Ladislav Rampásek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini. 2022 · 2022
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NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. In Proceedings of the Advances in Neural Information Processing Systems , Vol. 35. 27387–27401
Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, and Junchi Yan. 2022 · 2022
Later among the works it cites.
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. In Proceedings of the IEEE International Conference on Data Mining . 1287–1292
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra. 2022 · 2022
Later among the works it cites.
Hierarchical Graph Transformer with Adaptive Node Sampling. In Proceedings of the Advances in Neural Information Processing Systems , Vol. 35. 21171–21183
Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee. 2022 · 2022
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Specformer: Spectral Graph Neural Networks Meet Transformers. In Proceedings of the International Conference on Learning Representations
Deyu Bo, Chuan Shi, Lele Wang, and Renjie Liao. 2023 · 2023
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NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs. In Proceedings of the International Conference on Learning Representations
Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He. 2023 · 2023
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Shuhao Shi, Kai Qiao, Zhengyan Wang, Jie Yang, Baojie Song, Jian Chen, and Bin Yan. 2023 · 2023
Closest in time.