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This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.
“tri, tri again”: Finding triangles and small subgraphs in a distributed setting
Dolev, D., Lenzen, C., and Peled, S · 2012
Earlier work this paper cites.
On the power of the congested clique model
Drucker, A., Kuhn, F., and Oshman, R · 2014
Earlier work this paper cites.
Inductive representation learning on large graphs
Hamilton, W. L., Ying, Z., and Leskovec, J · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Mst in o (1) rounds of congested clique
Jurdziński, T. and Nowicki, K · 2018
Earlier work this paper cites.
Algebraic methods in the congested clique
Censor-Hillel, K., Kaski, P., Korhonen, J. H., Lenzen, C., Paz, A., and Suomela, J · 2019
Earlier work this paper cites.
Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
Earlier work this paper cites.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Cited alongside, same era.
The open catalyst 2020 (oc20) dataset and community challenges. arxiv
Chanussot, L., Da, A., Goyal, S., Lavril, T., Shuaibi, M., Riviere, M., Tran, K., Heras-Domingo, J., Ho, C., Hu, W., et al · 2020
Cited alongside, same era.
What graph neural networks cannot learn: depth vs width
Loukas, A · 2020
Cited alongside, same era.
On layer normalization in the transformer architecture
Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., and Liu, T · 2020
Cited alongside, same era.
An introduction to electrocatalyst design using machine learning for renewable energy storage
Zitnick, C. L., Chanussot, L., Das, A., Goyal, S., Heras-Domingo, J., Ho, C., Hu, W., Lavril, T., Palizhati, A., Riviere, M., et al · 2020
Cited alongside, same era.
Ogb-lsc: A large-scale challenge for machine learning on graphs
Hu, W., Fey, M., Ren, H., Nakata, M., Dong, Y., and Leskovec, J · 2021
Later among the works it cites.
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
Later among the works it cites.
Random features strengthen graph neural networks
Sato, R., Yamada, M., and Kashima, H · 2021
Later among the works it cites.
Rotation invariant graph neural networks using spin convolutions
Shuaibi, M., Kolluru, A., Das, A., Grover, A., Sriram, A., Ulissi, Z., and Zitnick, C. L · 2021
Later among the works it cites.
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
Later among the works it cites.
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The surprising power of graph neural networks with random node initialization
Abboud, R., Ceylan, I. I., Grohe, M., and Lukasiewicz, T · 2021
Cited alongside, same era.
Breaking the limits of message passing graph neural networks
Balcilar, M., Héroux, P., Gauzere, B., Vasseur, P., Adam, S., and Honeine, P · 2021
Cited alongside, same era.
Simple gnn regularisation for 3d molecular property prediction and beyond
Godwin, J., Schaarschmidt, M., Gaunt, A., Sanchez-Gonzalez, A., Rubanova, Y., Veličković, P., Kirkpatrick, J., and Battaglia, P · 2022
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