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In the realm of graph learning, there is a category of methods that conceptualize graphs as hierarchical structures, utilizing node clustering to capture broader structural information.
A fast and high quality multilevel scheme for partitioning irregular graphs
G. Karypis and V. Kumar · 1998
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Large scale multiple kernel learning
S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf · 2006
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Multiple kernel learning algorithms
M. Gönen and E. Alpaydin · 2011
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Order matters: Sequence to sequence for sets
O. Vinyals, S. Bengio, and M. Kudlur · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 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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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Y. Li, R. Yu, C. Shahabi, and Y. Liu · 2018
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Hierarchical graph representation learning with differentiable pooling
Z. Ying, J. You, C. Morris, X. Ren, W. L. Hamilton, and J. Leskovec · 2018
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
B. Yu, H. Yin, and Z. Zhu · 2018
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An end-to-end deep learning architecture for graph classification
M. Zhang, Z. Cui, M. Neumann, and Y. Chen · 2018
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A non-negative factorization approach to node pooling in graph convolutional neural networks
D. Bacciu and L. D. Sotto · 2019
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Fast graph representation learning with pytorch geometric
M. Fey and J. E. Lenssen · 2019
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Graph u-nets
H. Gao and S. Ji · 2019
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Self-attention graph pooling
J. Lee, I. Lee, and J. Kang · 2019
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Fake news detection on social media using geometric deep learning
F. Monti, F. Frasca, D. Eynard, D. Mannion, and M. M. Bronstein · 2019
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Transformer dissection: An unified understanding for transformer’s attention via the lens of kernel
Y. H. Tsai, S. Bai, M. Yamada, L. Morency, and R. Salakhutdinov · 2019
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Robust hierarchical graph classification with subgraph attention
S. Bandyopadhyay, M. Aggarwal, and M. N. Murty · 2020
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Spectral clustering with graph neural networks for graph pooling
F. M. Bianchi, D. Grattarola, and C. Alippi · 2020
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A fair comparison of graph neural networks for graph classification
F. Errica, M. Podda, D. Bacciu, and A. Micheli · 2020
Cited alongside, same era.
Hierarchical inter-message passing for learning on molecular graphs
M. Fey, J. Yuen, and F. Weichert · 2020
Cited alongside, same era.
Utilising graph machine learning within drug discovery and development
T. Gaudelet, B. Day, A. R. Jamasb, J. Soman, C. Regep, G. Liu, J. B. Hayter, R. Vickers, C. Roberts, J. Tang, et al · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
Cited alongside, same era.
Transformers are rnns: Fast autoregressive transformers with linear attention
A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret · 2020
Cited alongside, same era.
Graph neural networks with multiple kernel ensemble attention
H. Zhang and M. Xu · 2021
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Graph neural networks with learnable structural and positional representations
V. P. Dwivedi, A. T. Luu, T. Laurent, Y. Bengio, and X. Bresson · 2022
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Transformer for graphs: An overview from architecture perspective
E. Min, R. Chen, Y. Bian, T. Xu, K. Zhao, W. Huang, P. Zhao, J. Huang, S. Ananiadou, and Y. Rong · 2022
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cosformer: Rethinking softmax in attention
Z. Qin, W. Sun, H. Deng, D. Li, Y. Wei, B. Lv, J. Yan, L. Kong, and Y. Zhong · 2022
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Recipe for a general, powerful, scalable graph transformer
L. Rampásek, M. Galkin, V. P. Dwivedi, A. T. Luu, G. Wolf, and D. Beaini · 2022
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How powerful are spectral graph neural networks
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Rethinking pooling in graph neural networks
D. P. P. Mesquita, A. H. S. Jr., and S. Kaski · 2020
Cited alongside, same era.
Tudataset: A collection of benchmark datasets for learning with graphs
C. Morris, N. M. Kriege, F. Bause, K. Kersting, P. Mutzel, and M. Neumann · 2020
Cited alongside, same era.
Pinnersage: Multi-modal user embedding framework for recommendations at pinterest
A. Pal, C. Eksombatchai, Y. Zhou, B. Zhao, C. Rosenberg, and J. Leskovec · 2020
Cited alongside, same era.
ASAP: adaptive structure aware pooling for learning hierarchical graph representations
E. Ranjan, S. Sanyal, and P. P. Talukdar · 2020
Cited alongside, same era.
A deep learning approach to antibiotic discovery
J. M. Stokes, K. Yang, K. Swanson, W. Jin, A. Cubillos-Ruiz, N. M. Donghia, C. R. MacNair, S. French, L. A. Carfrae, Z. Bloom-Ackermann, et al · 2020
Cited alongside, same era.
Structpool: Structured graph pooling via conditional random fields
H. Yuan and S. Ji · 2020
Cited alongside, same era.
Accurate learning of graph representations with graph multiset pooling
J. Baek, M. Kang, and S. J. Hwang · 2021
Cited alongside, same era.
X. Wang and M. Zhang · 2022
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Structural entropy guided graph hierarchical pooling
J. Wu, X. Chen, K. Xu, and S. Li · 2022
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Hierarchical graph transformer with adaptive node sampling
Z. Zhang, Q. Liu, Q. Hu, and C. Lee · 2022
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Benchmarking graph neural networks
V. P. Dwivedi, C. K. Joshi, A. T. Luu, T. Laurent, Y. Bengio, and X. Bresson · 2023
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A generalization of vit/mlp-mixer to graphs
X. He, B. Hooi, T. Laurent, A. Perold, Y. LeCun, and X. Bresson · 2023
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Tailoring self-attention for graph via rooted subtrees
S. Huang, Y. Song, J. Zhou, and Z. Lin · 2023
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M. Jin, H. Y. Koh, Q. Wen, D. Zambon, C. Alippi, G. I. Webb, I. King, and S. Pan · 2023
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Gapformer: Graph transformer with graph pooling for node classification
C. Liu, Y. Zhan, X. Ma, L. Ding, D. Tao, J. Wu, and W. Hu · 2023
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Graph pooling for graph neural networks: Progress, challenges, and opportunities
C. Liu, Y. Zhan, J. Wu, C. Li, B. Du, W. Hu, T. Liu, and D. Tao · 2023
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Hierarchical adaptive pooling by capturing high-order dependency for graph representation learning
N. Liu, S. Jian, D. Li, Y. Zhang, Z. Lai, and H. Xu · 2023
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Exphormer: Sparse transformers for graphs
H. Shirzad, A. Velingker, B. Venkatachalam, D. J. Sutherland, and A. K. Sinop · 2023
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Understanding pooling in graph neural networks
D. Grattarola, D. Zambon, F. M. Bianchi, and C. Alippi · 2024
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Parsing netlists of integrated circuits from images via graph attention network
W. Hu, X. Zhan, and M. Tong · 2024
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Translating subgraphs to nodes makes simple gnns strong and efficient for subgraph representation learning
D. Kim and A. Oh · 2024
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