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Graph neural networks are powerful architectures for structured datasets.
Graph-Bert: Only Attention is Needed for Learning Graph Representations
J. Zhang, H. Zhang, C. Xia, and L. Sun · 2001
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End-to-End Object Detection with Transformers
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko · 2005
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Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification
N. Wale and G. Karypis · 2006
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Weighted Graph Cuts without Eigenvectors A Multilevel Approach
I. S. Dhillon, Y. Guan, and B. Kulis · 2007
Earlier work this paper cites.
Comparison of descriptor spaces for chemical compound retrieval and classification
N. Wale, I. A. Watson, and G. Karypis · 2008
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A Generalization of Transformer Networks to Graphs
V. P. Dwivedi and X. Bresson · 2012
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Order matters: Sequence to sequence for sets
O. Vinyals, S. Bengio, and M. Kudlur · 2016
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Keras-GCN
T. N. Kipf et al · 2017
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SGDR: stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, C. Gulcehre, F. Song, A. Ballard, J. Gilmer, G. Dahl, A. Vaswani, K. Allen, C. Nash, V. Langston, C. Dyer, N. Heess, D. Wierstra, P. Kohli, M. Botvinick, O. Vinyals, Y. Li, and R. Pascanu · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Q. Li, Z. Han, and X. Wu · 2018
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Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, and S. Jegelka · 2018
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
Z. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
Cited alongside, same era.
An end-to-end deep learning architecture for graph classification
M. Zhang, Z. Cui, M. Neumann, and Y. Chen · 2018
Cited alongside, same era.
Graph u-nets
H. Gao and S. Ji · 2019
Cited alongside, same era.
Attpool: Towards hierarchical feature representation in graph convolutional networks via attention mechanism
J. Huang, Z. Li, N. Li, S. Liu, and G. Li · 2019
Cited alongside, same era.
Self-attention graph pooling
J. Lee, I. Lee, and J. Kang · 2019
Cited alongside, same era.
Reformer: The efficient transformer
N. Kitaev, L. Kaiser, and A. Levskaya · 2020
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Rethinking pooling in graph neural networks
D. P. P. Mesquita, A. H. S. Jr., and S. Kaski · 2020
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Self-supervised graph transformer on large-scale molecular data
Y. Rong, Y. Bian, T. Xu, W. Xie, Y. Wei, W. Huang, and J. Huang · 2020
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HAT: Hardware-aware transformers for efficient natural language processing
H. Wang, Z. Wu, Z. Liu, H. Cai, L. Zhu, C. Gan, and S. Han · 2020
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Lite transformer with long-short range attention
Z. Wu, Z. Liu, J. Lin, Y. Lin, and S. Han · 2020
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Beyond homophily in graph neural networks: Current limitations and effective designs
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra · 2020
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
Cited alongside, same era.
Memory-based graph networks
A. H. K. Ahmadi, K. Hassani, P. Moradi, L. Lee, and Q. Morris · 2020
Cited alongside, same era.
Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
D. Chen, Y. Lin, W. Li, P. Li, J. Zhou, and X. Sun · 2020
Cited alongside, same era.
On the relationship between self-attention and convolutional layers
J. Cordonnier, A. Loukas, and M. Jaggi · 2020
Cited alongside, same era.
Principal neighbourhood aggregation for graph nets
G. Corso, L. Cavalleri, D. Beaini, P. Liò, and P. Velickovic · 2020
Cited alongside, same era.
A fair comparison of graph neural networks for graph classification
F. Errica, M. Podda, D. Bacciu, and A. Micheli · 2020
Cited alongside, same era.
On the bottleneck of graph neural networks and its practical implications
U. Alon and E. Yahav · 2021
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Rethinking attention with performers
K. M. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlós, P. Hawkins, J. Q. Davis, A. Mohiuddin, L. Kaiser, D. B. Belanger, L. J. Colwell, and A. Weller · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby · 2021
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Bottleneck Transformers for Visual Recognition
A. Srinivas, T.-Y. Lin, N. Parmar, J. Shlens, P. Abbeel, and A. Vaswani · 2021
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Adaptive filters and aggregator fusion for efficient graph convolutions
S. A. Tailor, F. L. Opolka, P. Liò, and N. D. Lane · 2021
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Directed acyclic graph neural networks
V. Thost and J. Chen · 2021
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Temporal Memory Attention for Video Semantic Segmentation
H. Wang, W. Wang, and J. Liu · 2021
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