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In many ways, graphs are the main modality of data we receive from nature.
Drug-drug adverse effect prediction with graph co-attention
Deac, A., Huang, Y.H., Veličković, P., Liò, P., Tang, J., 2019 · 1905
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Counterintuitive behavior of social systems
Forrester, J.W., 1971 · 1971
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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F., Worrall, D., Fischer, V., Welling, M., 2020 · 1981
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Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D.K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., Adams, R.P., 2015 · 2015
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Interaction networks for learning about objects, relations and physics
Battaglia, P., Pascanu, R., Lai, M., Jimenez Rezende, D., et al., 2016 · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., Vandergheynst, P., 2016 · 2016
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Neural message passing for quantum chemistry, in: International conference on machine learning, PMLR. pp. 1263–1272
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E., 2017 · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., Leskovec, J., 2017 · 2017
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Semi-supervised classification with graph convolutional networks, in: International Conference on Learning Representations
Kipf, T.N., Welling, M., 2017 · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5115–5124
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M., 2017 · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., 2017 · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J., 2017 · 2017
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Predicting multicellular function through multi-layer tissue networks
Zitnik, M., Leskovec, J., 2017 · 2017
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Relational inductive biases, deep learning, and graph networks
Battaglia, P.W., Hamrick, J.B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al., 2018 · 2018
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Neural relational inference for interacting systems, in: International Conference on Machine Learning, PMLR. pp. 2688–2697
Kipf, T., Fetaya, E., Wang, K.C., Welling, M., Zemel, R., 2018 · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., Riley, P., 2018 · 2018
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Graph attention networks, in: International Conference on Learning Representations
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y., 2018 · 2018
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Graph convolutional neural networks for web-scale recommender systems, in: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 974–983
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J., 2018 · 2018
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Modeling polypharmacy side effects with graph convolutional networks
Zitnik, M., Agrawal, M., Leskovec, J., 2018 · 2018
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Predicting drug–target interaction using a novel graph neural network with 3d structure-embedded graph representation
Lim, J., Ryu, S., Park, K., Choe, Y.J., Ham, J., Kim, W.Y., 2019 · 2019
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks, in: Proceedings of the AAAI conference on artificial intelligence, pp. 4602–4609
Morris, C., Ritzert, M., Fey, M., Hamilton, W.L., Lenssen, J.E., Rattan, G., Grohe, M., 2019 · 2019
Cited alongside, same era.
On the limitations of representing functions on sets, in: International Conference on Machine Learning, PMLR. pp. 6487–6494
Wagstaff, E., Fuchs, F., Engelcke, M., Posner, I., Osborne, M.A., 2019 · 2019
Cited alongside, same era.
Dynamic graph cnn for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M., 2019 · 2019
Cited alongside, same era.
Simplifying graph convolutional networks, in: International conference on machine learning, PMLR. pp. 6861–6871
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K., 2019 · 2019
Advancing mathematics by guiding human intuition with AI
Davies, A., Veličković, P., Buesing, L., Blackwell, S., Zheng, D., Tomašev, N., Tanburn, R., Battaglia, P., Blundell, C., Juhász, A., et al., 2021 · 2021
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Eta prediction with graph neural networks in google maps
Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., et al., 2021 · 2021
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Slaps: Self-supervision improves structure learning for graph neural networks
Fatemi, B., El Asri, L., Kazemi, S.M., 2021 · 2021
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The hardware lottery
Hooker, S., 2021 · 2021
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Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models
Jiang, D., Wu, Z., Hsieh, C.Y., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., Hou, T., 2021 · 2021
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Cited alongside, same era.
Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism
Xiong, Z., Wang, D., Liu, X., Zhong, F., Wan, X., Li, X., Li, Z., Luo, X., Chen, K., Jiang, H., et al., 2019 · 2019
Cited alongside, same era.
How powerful are graph neural networks?, in: International Conference on Learning Representations
Xu, K., Hu, W., Leskovec, J., Jegelka, S., 2019 · 2019
Cited alongside, same era.
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Gainza, P., Sverrisson, F., Monti, F., Rodola, E., Boscaini, D., Bronstein, M., Correia, B., 2020 · 2020
Cited alongside, same era.
P-companion: A principled framework for diversified complementary product recommendation
Hao, J., Zhao, T., Li, J., Dong, X.L., Faloutsos, C., Sun, Y., Wang, W., 2020 · 2020
Cited alongside, same era.
Drug–target affinity prediction using graph neural network and contact maps
Jiang, M., Li, Z., Zhang, S., Wang, S., Wang, X., Yuan, Q., Wei, Z., 2020 · 2020
Cited alongside, same era.
Transformers are graph neural networks
Joshi, C., 2020 · 2020
Cited alongside, same era.
What graph neural networks cannot learn: depth vs width
Loukas, A., 2020 · 2020
Cited alongside, same era.
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al., 2021 · 2021
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A geometric deep learning approach to predict binding conformations of bioactive molecules
Méndez-Lucio, O., Ahmad, M., del Rio-Chanona, E.A., Wegner, J.K., 2021 · 2021
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Graph networks for molecular design
Mercado, R., Rastemo, T., Lindelöf, E., Klambauer, G., Engkvist, O., Chen, H., Bjerrum, E.J., 2021 · 2021
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A graph placement methodology for fast chip design
Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J.W., Songhori, E., Wang, S., Lee, Y.J., Johnson, E., Pathak, O., Nazi, A., et al., 2021 · 2021
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Network medicine framework for identifying drug-repurposing opportunities for covid-19
Morselli Gysi, D., Do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S.D., Patten, J., Davey, R.A., Loscalzo, J., et al., 2021 · 2021
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E (n) equivariant graph neural networks
Satorras, V.G., Hoogeboom, E., Welling, M., 2021 · 2021
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Learning graph models for retrosynthesis prediction
Somnath, V.R., Bunne, C., Coley, C., Krause, A., Barzilay, R., 2021 · 2021
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How attentive are graph attention networks?, in: International Conference on Learning Representations
Brody, S., Alon, U., Yahav, E., 2022 · 2022
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Expander graph propagation, in: The First Learning on Graphs Conference
Deac, A., Lackenby, M., Veličković, P., 2022 · 2022
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Differentiable graph module (dgm) for graph convolutional networks
Kazi, A., Cosmo, L., Ahmadi, S.A., Navab, N., Bronstein, M., 2022 · 2022
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Retrognn: Fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software
Liu, C.H., Korablyov, M., Jastrzebski, S., Włodarczyk-Pruszynski, P., Bengio, Y., Segler, M., 2022 · 2022
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Equibind: Geometric deep learning for drug binding structure prediction, in: International Conference on Machine Learning, PMLR. pp. 20503–20521
Stärk, H., Ganea, O., Pattanaik, L., Barzilay, R., Jaakkola, T., 2022 · 2022
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Message passing all the way up
Veličković, P., 2022 · 2022
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