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Despite recent successes in natural language processing and computer vision, Transformer suffers from the scalability problem when dealing with graphs.
Improving graph attention networks with large margin-based constraints
Wang, G., Ying, R., Huang, J., and Leskovec, J · 1910
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Coke: Contextualized knowledge graph embedding
Wang, Q., Huang, P., Wang, H., Dai, S., Jiang, W., Liu, J., Lyu, Y., Zhu, Y., and Wu, H · 1911
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A learning algorithm for continually running fully recurrent neural networks
Williams, R. J. and Zipser, D · 1989
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WordNet: An electronic lexical database
Miller, G. A · 1995
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Laurent, T., Bengio, Y., and Bresson, X · 2003
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Freebase: A collaboratively created graph database for structuring human knowledge
Bollacker, K. D., Evans, C., Paritosh, P., Sturge, T., and Taylor, J · 2008
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Translating embeddings for modeling multi-relational data
Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., and Yakhnenko, O · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Observed versus latent features for knowledge base and text inference
Toutanova, K. and Chen, D · 2015
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W. W., and Salakhutdinov, R · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, Z., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Cited alongside, same era.
Pubchemqc project: a large-scale first-principles electronic structure database for data-driven chemistry
Nakata, M. and Shimazaki, T · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
Zitnik, M. and Leskovec, J · 2017
Cited alongside, same era.
Convolutional 2D knowledge graph embeddings
Dettmers, T., Minervini, P., Stenetorp, P., and Riedel, S · 2018
Cited alongside, same era.
Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Cited alongside, same era.
Knowledge graph alignment network with gated multi-hop neighborhood aggregation
Sun, Z., Wang, C., Hu, W., Chen, M., Dai, J., Zhang, W., and Qu, Y · 2020
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Composition-based multi-relational graph convolutional networks
Vashishth, S., Sanyal, S., Nitin, V., and Talukdar, P. P · 2020
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Robust graph representation learning via neural sparsification
Zheng, C., Zong, B., Cheng, W., Song, D., Ni, J., Yu, W., Chen, H., and Wang, W · 2020
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Hitter: Hierarchical transformers for knowledge graph embeddings
Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., and Ji, Y · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Tucker: Tensor factorization for knowledge graph completion
Balazevic, I., Allen, C., and Hospedales, T. M · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
Cited alongside, same era.
Rotate: Knowledge graph embedding by relational rotation in complex space
Sun, Z., Deng, Z.-H., Nie, J.-Y., and Tang, J · 2019
Cited alongside, same era.
Relation-aware entity alignment for heterogeneous knowledge graphs
Wu, Y., Liu, X., Feng, Y., Wang, Z., Yan, R., and Zhao, D · 2019
Cited alongside, same era.
End-to-end open-domain question answering with bertserini
Yang, W., Xie, Y., Lin, A., Li, X., Tan, L., Xiong, K., Li, M., and Lin, J · 2019
Cited alongside, same era.
Dwivedi, V. P. and Bresson, X · 2021
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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
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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., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., and Hassabis, D · 2021
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How to find your friendly neighborhood: Graph attention design with self-supervision
Kim, D. and Oh, A. H · 2021
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Rethinking graph transformers with spectral attention
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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Msa transformer
Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., and Rives, A · 2021
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Do transformers really perform badly 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.