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Graph transformers typically lack third-order interactions, limiting their geometric understanding which is crucial for tasks like molecular geometry prediction.
Pattern classification and scene analysis , volume 3
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
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Moleculenet: a benchmark for molecular machine learning
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
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Cormorant: Covariant molecular neural networks
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Generating long sequences with sparse transformers
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Albert: A lite bert for self-supervised learning of language representations
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Pytorch: An imperative style, high-performance deep learning library
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Graph convolutions that can finally model local structure
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An image is worth 16x16 words: Transformers for image recognition at scale
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Se (3)-transformers: 3d roto-translation equivariant attention networks
On the global self-attention mechanism for graph convolutional networks
Wang, C. and Deng, C · 2021
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Representing long-range context for graph neural networks with global attention
Wu, Z., Jain, P., Wright, M., Mirhoseini, A., Gonzalez, J. E., and Stoica, I · 2021
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Do transformers really perform bad for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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Dropattention: a regularization method for fully-connected self-attention networks
Zehui, L., Liu, P., Huang, L., Chen, J., Qiu, X., and Huang, X · 2021
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Global self-attention as a replacement for graph convolution
Hussain, M. S., Zaki, M. J., and Subramanian, D · 2022
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Fuchs, F., Worrall, D., Fischer, V., and Welling, M · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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Flag: Adversarial data augmentation for graph neural networks
Kong, K., Li, G., Ding, M., Wu, Z., Zhu, C., Ghanem, B., Taylor, G., and Goldstein, T · 2020
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Deepergcn: All you need to train deeper gcns
Li, G., Xiong, C., Thabet, A., and Ghanem, B · 2020
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Self-supervised graph transformer on large-scale molecular data
Rong, Y., Bian, Y., Xu, T., Xie, W., Wei, Y., Huang, W., and Huang, J · 2020
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Lit-pcba: an unbiased data set for machine learning and virtual screening
Tran-Nguyen, V.-K., Jacquemard, C., and Rognan, D · 2020
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Directional graph networks
Beani, D., Passaro, S., Létourneau, V., Hamilton, W., Corso, G., and Liò, P · 2021
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Geometric and physical quantities improve e (3) equivariant message passing
Brandstetter, J., Hesselink, R., van der Pol, E., Bekkers, E. J., and Welling, M · 2021
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3d equivariant molecular graph pretraining
Jiao, R., Han, J., Huang, W., Rong, Y., and Liu, Y · 2022
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Pure transformers are powerful graph learners
Kim, J., Nguyen, D., Min, S., Cho, S., Lee, M., Lee, H., and Hong, S · 2022
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Gns: A generalizable graph neural network-based simulator for particulate and fluid modeling
Kumar, K. and Vantassel, J · 2022
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Equivariant graph attention networks for molecular property prediction
Le, T., Noé, F., and Clevert, D.-A · 2022
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One transformer can understand both 2d & 3d molecular data
Luo, S., Chen, T., Xu, Y., Zheng, S., Liu, T.-Y., Wang, L., and He, D · 2022
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Gps++: An optimised hybrid mpnn/transformer for molecular property prediction
Masters, D., Dean, J., Klaser, K., Li, Z., Maddrell-Mander, S., Sanders, A., Helal, H., Beker, D., Rampášek, L., and Beaini, D · 2022
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Grpe: Relative positional encoding for graph transformer
Park, W., Chang, W.-G., Lee, D., Kim, J., et al · 2022
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Recipe for a general, powerful, scalable graph transformer
Rampášek, L., Galkin, M., Dwivedi, V. P., Luu, A. T., Wolf, G., and Beaini, D · 2022
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Benchmarking graphormer on large-scale molecular modeling datasets
Shi, Y., Zheng, S., Ke, G., Shen, Y., You, J., He, J., Luo, S., Liu, C., He, D., and Liu, T.-Y · 2022
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3d infomax improves gnns for molecular property prediction
Stärk, H., Beaini, D., Corso, G., Tossou, P., Dallago, C., Günnemann, S., and Liò, P · 2022
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Torchmd-net: equivariant transformers for neural network based molecular potentials
Thölke, P. and De Fabritiis, G · 2022
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The information pathways hypothesis: Transformers are dynamic self-ensembles
Hussain, M. S., Zaki, M. J., and Subramanian, D · 2023
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On the expressive power of geometric graph neural networks
Joshi, C. K., Bodnar, C., Mathis, S. V., Cohen, T., and Lio, P · 2023
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Edge-enhanced attentions for drone delivery in presence of winds and recharging stations
Liu, R., Shin, H.-S., and Tsourdos, A · 2023
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Highly accurate quantum chemical property prediction with uni-mol+
Lu, S., Gao, Z., He, D., Zhang, L., and Ke, G · 2023
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Molecular geometry-aware transformer for accurate 3d atomic system modeling
Yuan, Z., Zhang, Y., Tan, C., Wang, W., Huang, F., and Huang, S · 2023
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Is distance matrix enough for geometric deep learning?
Li, Z., Wang, X., Huang, Y., and Zhang, M · 2024
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