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We introduceGraphGPT, a novel self-supervised generative pre-trained model for graph learning based on the Graph Eulerian Transformer (GET).
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Neural message passing for quantum chemistry
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Inductive representation learning on large graphs
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The effectiveness of data augmentation in image classification using deep learning
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Attention is all you need
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Moleculenet: A benchmark for molecular machine learning
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Improving language understanding by generative pre-training, 2018
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Link prediction based on graph neural networks
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BERT: pre-training of deep bidirectional transformers for language understanding
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Fast graph representation learning with PyTorch Geometric
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Understanding attention and generalization in graph neural networks
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2019
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Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Language models are few-shot learners
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The graph isomorphism problem
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Adversarial attacks and defenses on graphs
Jin, W., Li, Y., Xu, H., Wang, Y., Ji, S., Aggarwal, C., and Tang, J · 2020
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Scaling laws for neural language models
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Recipe for a general, powerful, scalable graph transformer
Rampásek, L., Galkin, M., Dwivedi, V. P., Luu, A. T., Wolf, G., and Beaini, D · 2022
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On the adversarial robustness of vision transformers
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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 · 2022
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Finetuned language models are zero-shot learners
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GCC: graph contrastive coding for graph neural network pre-training
Qiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H., Ding, M., Wang, K., and Tang, J · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J., Rajbhandari, S., Ruwase, O., and He, Y · 2020
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Karate club: An API oriented open-source python framework for unsupervised learning on graphs
Rozemberczki, B., Kiss, O., and Sarkar, R · 2020
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 2020
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y · 2020
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Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2022
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Nodeformer: A scalable graph structure learning transformer for node classification
Wu, Q., Zhao, W., Li, Z., Wipf, D. P., and Yan, J · 2022
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Nagphormer: A tokenized graph transformer for node classification in large graphs
Chen, J., Gao, K., Li, G., and He, K · 2023
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Graph propagation transformer for graph representation learning
Chen, Z., Tan, H., Wang, T., Shen, T., Lu, T., Peng, Q., Cheng, C., and Qi, Y · 2023
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Goat: A global transformer on large-scale graphs
Kong, K., Chen, J., Kirchenbauer, J., Ni, R., Bruss, C. B., and Goldstein, T · 2023
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Deepergcn: Training deeper gcns with generalized aggregation functions
Li, G., Xiong, C., Qian, G., Thabet, A. K., and Ghanem, B · 2023
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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 · 2023
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GPS++: reviving the art of message passing for molecular property prediction
Masters, D., Dean, J., Kläser, K., Li, Z., Maddrell-Mander, S., Sanders, A., Helal, H., Beker, D., Fitzgibbon, A. W., Huang, S., Rampásek, L., and Beaini, D · 2023
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Midjourney, 2023
Midjourney, I · 2023
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A survey on oversmoothing in graph neural networks
Rusch, T. K., Bronstein, M. M., and Mishra, S · 2023
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Exphormer: Sparse transformers for graphs
Shirzad, H., Velingker, A., Venkatachalam, B., Sutherland, D. J., and Sinop, A. K · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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Scientific discovery in the age of artificial intelligence
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Katwyk, P. V., Deac, A., Anandkumar, A., Bergen, K., Gomes, C. P., Ho, S., Kohli, P., Lasenby, J., Leskovec, J., Liu, T., Manrai, A., Marks, D. S., Ramsundar, B., Song, L., Sun, J., Tang, J., Velickovic, P., Welling, M., Zhang, L., Coley, C. W., Bengio, Y., and Zitnik, M · 2023
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Towards better graph representation learning with parameterized decomposition & filtering
Yang, M., Feng, W., Shen, Y., and Hooi, B · 2023
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DRGCN: dynamic evolving initial residual for deep graph convolutional networks
Zhang, L., Yan, X., He, J., Li, R., and Chu, W · 2023
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Flashattention-2: Faster attention with better parallelism and work partitioning
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Polynormer: Polynomial-expressive graph transformer in linear time
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Pure message passing can estimate common neighbor for link prediction
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Better & faster large language models via multi-token prediction
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Classic gnns are strong baselines: Reassessing gnns for node classification
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Reconsidering the performance of GAE in link prediction
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Attending to graph transformers
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Structural information enhanced graph representation for link prediction
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Simplifying and empowering transformers for large-graph representations
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Dataset ogbl-ppa
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