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Graph Transformers (GTs) have recently achieved significant success in the graph domain by effectively capturing both long-range dependencies and graph inductive biases.
Revisiting Semi-Supervised Learning with Graph Embeddings. In Proc. of ICML . 40–48
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. 2016 · 2016
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Neural Message Passing for Quantum Chemistry. In Proc. of ICML . 1263–1272
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017 · 2017
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Inductive Representation Learning on Large Graphs. In Proc. of NeurIPS . 1024–1034
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
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Semi-Supervised Classification with Graph Convolutional Networks. In Proc. of ICLR
Thomas N. Kipf and Max Welling. 2017 · 2017
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DeepInf: Social Influence Prediction with Deep Learning. In Proc. of KDD
Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018 · 2018
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Graph Attention Networks. In Proc. of ICLR
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
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Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In Proc. of KDD
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
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Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen. 2019 · 2019
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Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks. In Proc. of AAAI . 4602–4609
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019 · 2019
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How Powerful are Graph Neural Networks?. In Proc. of ICLR
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
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Simple and Deep Graph Convolutional Networks. In Proc. of ICML . 1725–1735
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020 · 2020
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A Generalization of Transformer Networks to Graphs
Vijay Prakash Dwivedi and Xavier Bresson. 2020 · 2020
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Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2020 · 2020
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Graph Random Neural Network for Semi-Supervised Learning on Graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020 · 2020
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Open Graph Benchmark: Datasets for Machine Learning on Graphs. In Proc. of NeurIPS
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
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Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. In Proc. of NeurIPS
Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
Towards Deeper Graph Neural Networks. In Proc. of KDD
Meng Liu, Hongyang Gao, and Shuiwang Ji. 2020 · 2020
Cited alongside, same era.
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In Proc. of ICLR
Kenta Oono and Taiji Suzuki. 2020 · 2020
Cited alongside, same era.
Geom-GCN: Geometric Graph Convolutional Networks. In Proc. of ICLR
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020 · 2020
Cited alongside, same era.
A Deep Learning Approach to Antibiotic Discovery
Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, and James J. Collins. 2020 · 2020
Cited alongside, same era.
Structure-Aware Transformer for Graph Representation Learning. In Proc. of ICML . 3469–3489
Dexiong Chen, Leslie O’Bray, and Karsten M. Borgwardt. 2022 · 2022
Later among the works it cites.
GRAND+: Scalable Graph Random Neural Networks
Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, and Jie Tang. 2022 · 2022
Later among the works it cites.
Global Self-Attention as a Replacement for Graph Convolution. In Proc. of KDD
Md Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian. 2022 · 2022
Later among the works it cites.
Understanding over-squashing and bottlenecks on graphs via curvature. In Proc. of ICLR
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein. 2022 · 2022
Later among the works it cites.
RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning. In Proc. of IJCAI . 3795–3801
Yun Zhu, Jianhao Guo, Fei Wu, and Siliang Tang. 2022 · 2022
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Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna. 2020 · 2020
Cited alongside, same era.
Adaptive Structural Fingerprints for Graph Attention Networks. In Proc. of ICLR
Kai Zhang, Yaokang Zhu, Jun Wang, and Jie Zhang. 2020 · 2020
Cited alongside, same era.
On the Bottleneck of Graph Neural Networks and its Practical Implications. In Proc. of ICLR
Uri Alon and Eran Yahav. 2021 · 2021
Cited alongside, same era.
Adaptive Universal Generalized PageRank Graph Neural Network. In Proc. of ICLR
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021 · 2021
Cited alongside, same era.
ETA Prediction with Graph Neural Networks in Google Maps. In Proc. of CIKM
Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, Peter W. Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, and Petar Velickovic. 2021 · 2021
Cited alongside, same era.
Learning Conjoint Attentions for Graph Neural Nets. In Proc. of NeurIPS . 2641–2653
Tiantian He, Yew Soon Ong, and Lu Bai. 2021 · 2021
Cited alongside, same era.
Rethinking Graph Transformers with Spectral Attention. In Proc. of NeurIPS . 21618–21629
Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, and Prudencio Tossou. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M. Bronstein. 2023 · 2023
Later among the works it cites.
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He. 2023 · 2023
Later among the works it cites.
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi. 2023 · 2023
Later among the works it cites.
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
Soo Yong Lee, Fanchen Bu, Jaemin Yoo, and Kijung Shin. 2023 · 2023
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 Torr, and Ser-Nam Lim. 2023 · 2023
Later among the works it cites.
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini. 2023 · 2023
Later among the works it cites.
Xiaotang Wang, Yun Zhu, Haizhou Shi, Yongchao Liu, and Chuntao Hong. 2024 · 2024
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Less is More: on the Over-Globalizing Problem in Graph Transformers
Yujie Xing, Xiao Wang, Yibo Li, Hai Huang, and Chuan Shi. 2024 · 2024
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GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning. In Proceedings of the ACM on Web Conference 2024 . 539–550
Yun Zhu, Yaoke Wang, Haizhou Shi, Zhenshuo Zhang, Dian Jiao, and Siliang Tang. 2024c · 2024
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