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Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks.
Harmonische funktionen und randwertaufgaben in einem komplex
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The predictive toxicology challenge 2000–2001
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Network motifs: Simple building blocks of complex networks
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Distinguishing enzyme structures from non-enzymes without alignments
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On graph kernels: Hardness results and efficient alternatives
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The graph neural network model
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Convolutional networks on graphs for learning molecular fingerprints
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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Signal transduction in cancer
R. Sever and J. S. Brugge · 2015
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Hodge Laplacians on graphs
L. H. Lim · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Diffusion-convolutional neural networks
J. Atwood and D. Towsley · 2016
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Two’s company, three (or more) is a simplex
C. Giusti, R. Ghrist, and D. S. Bassett · 2016
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Propagation kernels: efficient graph kernels from propagated information
M. Neumann, R. Garnett, C. Bauckhage, and K. Kersting · 2016
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Neural message passing for quantum chemistry
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Semi-Supervised Classification with Graph Convolutional Networks
T. N. Kipf and M. Welling · 2017
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Inductive representation learning on large graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Link prediction based on graph neural networks
M. Zhang and Y. Chen · 2018
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Cliques and cavities in the human connectome
A.E. Sizemore, C. Giusti, A. Kahn, J.M. Vettel, R. F. Betzel, and D.S. Bassett · 2018
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Hyper-sagnn: a self-attention based graph neural network for hypergraphs
R. Zhang, Y. Zou, and J. Ma · 2020
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Neural message passing for multi-relational ordered and recursive hypergraphs
N. Yadati · 2020
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Hypergraph attention networks for multimodal learning
E.S Kim, W.Y. Kang, K.W. On, Y.J Heo, and B.T Zhang · 2020
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Hypergraph attention networks
C. Chen, Z. Cheng, Z. Li, and M. Wang · 2020
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Topological signal processing over simplicial complexes
S. Barbarossa and S. Sardellitti · 2020
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Out-of-sample representation learning for knowledge graphs
M. Albooyeh, R. Goel, and S. M. Kazemi · 2020
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Y. Feng, H. You, Z. Zhang, R. Ji, and Y. Gao · 2018
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Towards sparse hierarchical graph classifiers
C. Cangea, P. Veličković, N. Jovanović, T. Kipf, and P. Liò · 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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Invariant and equivariant graph networks
H. Maron, H. Ben-Hamu, N. Shamir, and Y. Lipman · 2018
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Graph neural networks for social recommendation
W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin · 2019
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Graph transformer networks, 2019
S. Yun, M. Jeong, R. Kim, J. Kang, and Hyunwoo J. Kim · 2019
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C. Morris, N. M. Kriege, F. Bause, K. Kersting, P. Mutzel, and M. Neumann · 2020
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Natural graph networks
P. de Haan, T.S. Cohen, and M. Welling · 2020
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Highly accurate protein structure prediction with alphafold
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, et al · 2021
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Advancing mathematics by guiding human intuition with ai
A. Davies, P. Veličković, L. Buesing, S. Blackwell, D. Zheng, N. Tomašev, R. Tanburn, P. Battaglia, C. Blundell, A. Juhász, et al · 2021
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Neural algorithmic reasoning
P. Veličković and C. Blundell · 2021
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Edgenets: Edge varying graph neural networks
Ribeiro A. Isufi E., Gama F · 2021
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Topological signal processing over cell complexes
S. Sardellitti, S. Barbarossa, and L. Testa · 2021
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How attentive are graph attention networks?
Shaked Brody, Uri Alon, and Eran Yahav · 2021
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You are allset: A multiset function framework for hypergraph neural networks
E. Chien, C. Pan, J. Peng, and O. Milenkovic · 2022
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C. Wei Jin Goh, C. Bodnar, and P. Lio · 2022
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Improving graph neural network expressivity via subgraph isomorphism counting
G. Bouritsas, F. Frasca, S.P. Zafeiriou, and M. Bronstein · 2022
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Graph neural networks are more powerful than we think, 2022
C.I. Kanatsoulis and A. Ribeiro · 2022
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Parallel and distributed graph neural networks: An in-depth concurrency analysis, 2022
M. Besta and T. Hoefler · 2022
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