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The transformer architecture has shown remarkable success in various domains, such as natural language processing and computer vision.
Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
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Story embedding: Learning distributed representations of stories based on character networks (extended abstract)
O-Joun Lee and Jason J. Jung · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2021
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Learning multi-resolution representations of research patterns in bibliographic networks
O-Joun Lee, Hyeon-Ju Jeon, and Jason J. Jung · 2021
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Plot structure decomposition in narrative multimedia by analyzing personalities of fictional characters
O-Joun Lee, Eun-Soon You, and Jin-Taek Kim · 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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Masked label prediction: Unified message passing model for semi-supervised classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjing Wang, and Yu Sun · 2021
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Representing long-range context for graph neural networks with global attention
Zhanghao Wu, Paras Jain, Matthew A. Wright, Azalia Mirhoseini, Joseph E. Gonzalez, and Ion Stoica · 2021
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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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Graphit: Encoding graph structure in transformers
Grégoire Mialon, Dexiong Chen, Margot Selosse, and Julien Mairal · 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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Structure-aware transformer for graph representation learning
Dexiong Chen, Leslie O’Bray, and Karsten M. Borgwardt · 2022
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Day-ahead hourly solar irradiance forecasting based on multi-attributed spatio-temporal graph convolutional network
Hyeon-Ju Jeon, Min-Woo Choi, and O-Joun Lee · 2022
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Coarformer: Transformer for large graph via graph coarsening
Weirui Kuang, Zhen WANG, Yaliang Li, Zhewei Wei, and Bolin Ding · 2022
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Global self-attention as a replacement for graph convolution
Md. Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian · 2022
Cited alongside, same era.
Recipe for a general, powerful, scalable graph transformer
Ladislav Rampásek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
Cited alongside, same era.
Universal graph transformer self-attention networks
Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung · 2022
Cited alongside, same era.
Pure transformers are powerful graph learners
Jinwoo Kim, Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong · 2022
Cited alongside, same era.
Grpe: Relative positional encoding for graph transformer
Wonpyo Park, Woong-Gi Chang, Donggeon Lee, Juntae Kim, and Seungwon Hwang · 2022
Cited alongside, same era.
Graph neural networks with learnable structural and positional representations
Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2022
Random walk conformer: Learning graph representation from long and short range
Pei-Kai Yeh, Hsi-Wen Chen, and Ming-Syan Chen · 2023
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Rethinking the expressive power of gnns via graph biconnectivity
Bohang Zhang, Shengjie Luo, Liwei Wang, and Di He · 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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Exphormer: Sparse transformers for graphs
Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, and Ali Kemal Sinop · 2023
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Are more layers beneficial to graph transformers?
Haiteng Zhao, Shuming Ma, Dongdong Zhang, Zhi-Hong Deng, and Furu Wei · 2023
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Nagphormer: A tokenized graph transformer for node classification in large graphs
Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He · 2023
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Cited alongside, same era.
Hierarchical graph transformer with adaptive node sampling
Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee · 2022
Cited alongside, same era.
Graph representation learning and its applications: A survey
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon, Sungyeop Jung, and O-Joun Lee · 2023
Cited alongside, same era.
Revisiting over-smoothing and over-squashing using ollivier-ricci curvature
Khang Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, Nhat Ho, Stanley J. Osher, and Tan Minh Nguyen · 2023
Cited alongside, same era.
Mitigating degree biases in message passing mechanism by utilizing community structures
Van Thuy Hoang and O-Joun Lee · 2023
Cited alongside, same era.
Companion animal disease diagnostics based on literal-aware medical knowledge graph representation learning
Van Thuy Hoang, Thanh Sang Nguyen, Sangmyeong Lee, Jooho Lee, Luong Vuong Nguyen, and O-Joun Lee · 2023
Cited alongside, same era.
Connector 0.5: A unified framework for graph representation learning
Thanh-Sang Nguyen, Jooho Lee, Van Thuy Hoang, and O-Joun Lee · 2023
Cited alongside, same era.
Later among the works it cites.
Graph inductive biases in transformers without message passing
Liheng Ma, Chen Lin, Derek Lim, Adriana Romero-Soriano, Puneet K. Dokania, Mark Coates, Philip H. S. Torr, and Ser-Nam Lim · 2023
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Agformer: Efficient graph representation with anchor-graph transformer
Bo Jiang, Fei Xu, Ziyan Zhang, Jin Tang, and Feiping Nie · 2023
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Gapformer: Graph transformer with graph pooling for node classification
Chuang Liu, Yibing Zhan, Xueqi Ma, Liang Ding, Dapeng Tao, Jia Wu, and Wenbin Hu · 2023
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Sign and basis invariant networks for spectral graph representation learning
Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, and Stefanie Jegelka · 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
Later among the works it cites.
A survey on efficient training of transformers
Bohan Zhuang, Jing Liu, Zizheng Pan, Haoyu He, Yuetian Weng, and Chunhua Shen · 2023
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Transitivity-preserving graph representation learning for bridging local connectivity and role-based similarity
Van Thuy Hoang and O-Joun Lee · 2024
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