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By incorporating the graph structural information into Transformers, graph Transformers have exhibited promising performance for graph representation learning in recent years.
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.
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.
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, 2017
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, 2017
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, 2017
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, 2018
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 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.
Diffusion Improves Graph Learning. In Proceedings of the Advances in Neural Information Processing Systems, 2019
Johannes Gasteiger, Stefan Weißenberger, and Stephan Günnemann. 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
Cited alongside, same era.
Decoupled Weight Decay Regularization. In Proceedings of the International Conference on Learning Representations, 2019
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Cited alongside, same era.
Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020 . 3438–3445
Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun. 2020 · 2020
Cited alongside, same era.
Multi-head attention: Collaborate instead of concatenate
Jean-Baptiste Cordonnier, Andreas Loukas, and Martin Jaggi. 2020 · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs. In Proceedings of the Advances in Neural Information Processing Systems, 2020
Representing Long-Range Context for Graph Neural Networks with Global Attention. In Proceedings of the Advances in Neural Information Processing Systems, 2021
Paras Jain, Zhanghao Wu, Matthew Wright, Azalia Mirhoseini, Joseph E Gonzalez, and Ion Stoica. 2021 · 2021
Later among the works it cites.
Rethinking Graph Transformers with Spectral Attention. In Proceedings of the Advances in Neural Information Processing Systems, 2021
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou. 2021 · 2021
Later among the works it cites.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021 · 2021
Later among the works it cites.
Graphit: Encoding Graph Structure in Transformers
Grégoire Mialon, Dexiong Chen, Margot Selosse, and Julien Mairal. 2021 · 2021
Later among the works it cites.
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Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 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.
Self-supervised graph transformer on large-scale molecular data. In Proceedings of the Advances in Neural Information Processing Systems, 2020
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020 · 2020
Cited alongside, same era.
On the Bottleneck of Graph Neural Networks and its Practical Implications. In Proceedings of the 9th International Conference on Learning Representations, 2021
Uri Alon and Eran Yahav. 2021 · 2021
Cited alongside, same era.
A Generalization of Transformer Networks to Graphs. In Proceedings of the AAAI Workshop on Deep Learning on Graphs: Methods and Applications, 2021
Vijay Prakash Dwivedi and Xavier Bresson. 2021 · 2021
Cited alongside, same era.
Do Transformers Really Perform Badly for Graph Representation. In Proceedings of the Advances in Neural Information Processing Systems, 2021
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.
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 · 2021
Later among the works it cites.
Structure-Aware Transformer for Graph Representation Learning. In Proceedings of the International Conference on Machine Learning, 2022
Dexiong Chen, Leslie O’Bray, and Karsten Borgwardt. 2022 · 2022
Closest in time.
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 · 2022
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GRPE: Relative Positional Encoding for Graph Transformer. In ICLR Machine Learning for Drug Discovery, 2022
Wonpyo Park, Woong-Gi Chang, Donggeon Lee, Juntae Kim, et al · 2022
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