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Can we model Non-Euclidean graphs as pure language or even Euclidean vectors while retaining their inherent information? The Non-Euclidean property have posed a long term challenge in graph modeling.
Self-supervised graph representation learning via global context prediction
Peng, Z., Dong, Y., Luo, M., Wu, X.-M., and Zheng, Q · 2003
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Zinc- a free database of commercially available compounds for virtual screening
Irwin, J. J. and Shoichet, B. K · 2005
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Distributed large-scale natural graph factorization
Ahmed, A., Shervashidze, N., Narayanamurthy, S., Josifovski, V., and Smola, A. J · 2013
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Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
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node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Accelerated hierarchical density based clustering
McInnes, L. and Healy, J · 2017
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Searching for activation functions
Ramachandran, P., Zoph, B., and Le, Q. V · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Chen, J., Ma, T., and Xiao, C · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
McInnes, L., Healy, J., and Melville, J · 2018
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Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
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Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J · 2018
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Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C · 2019
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J · 2019
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Edge contraction pooling for graph neural networks
Diehl, F · 2019
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Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2019
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Self-attention graph pooling
Lee, J., Lee, I., and Kang, J · 2019
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Graph convolutional networks with eigenpooling
Ma, Y., Wang, S., Aggarwal, C. C., and Tang, J · 2019
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Graphaf: a flow-based autoregressive model for molecular graph generation
Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., and Tang, J · 2019
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Sun, F.-Y., Hoffmann, J., Verma, V., and Tang, J · 2019
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Simplifying graph convolutional networks
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K · 2019
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Root mean square layer normalization
Zhang, B. and Sennrich, R · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Infinitewalk: Deep network embeddings as laplacian embeddings with a nonlinearity
Chanpuriya, S. and Musco, C · 2020
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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., et al · 2020
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A generalization of transformer networks to graphs
Dwivedi, V. P. and Bresson, X · 2020
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Heterogeneous graph transformer
Hu, Z., Dong, Y., Wang, K., and Sun, Y · 2020
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Self-supervised auxiliary learning with meta-paths for heterogeneous graphs
Hwang, D., Park, J., Kwon, S., Kim, K., Ha, J.-W., and Kim, H. J · 2020
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Self-supervised learning on graphs: Deep insights and new direction
Jin, W., Derr, T., Liu, H., Wang, Y., Wang, S., Liu, Z., and Tang, J · 2020
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Permutation invariant graph generation via score-based generative modeling
Niu, C., Song, Y., Song, J., Zhao, S., Grover, A., and Ermon, S · 2020
Cited alongside, same era.
Graph representation learning via graphical mutual information maximization
Peng, Z., Huang, W., Luo, M., Zheng, Q., Rong, Y., Xu, T., and Huang, J · 2020
Cited alongside, same era.
Molecular sets (moses): a benchmarking platform for molecular generation models
Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al · 2020
Cited alongside, same era.
3dlinker: an e (3) equivariant variational autoencoder for molecular linker design
Huang, Y., Peng, X., Ma, J., and Zhang, M · 2022
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Score-based generative modeling of graphs via the system of stochastic differential equations
Jo, J., Lee, S., and Hwang, S. J · 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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Improving graph collaborative filtering with neighborhood-enriched contrastive learning
Lin, Z., Tian, C., Hou, Y., and Zhao, W. X · 2022
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Graph self-supervised learning: A survey
Liu, Y., Jin, M., Pan, S., Zhou, C., Zheng, Y., Xia, F., and Philip, S. Y · 2022
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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
Cited alongside, same era.
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
Cited alongside, same era.
Graphaf: a flow-based autoregressive model for molecular graph generation
Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., and Tang, J · 2020
Cited alongside, same era.
Vertex-reinforced random walk for network embedding
Xiao, W., Zhao, H., Zheng, V. W., and Song, Y · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
Cited alongside, same era.
Moflow: an invertible flow model for generating molecular graphs
Zang, C. and Wang, F · 2020
Cited alongside, same era.
Deep graph contrastive representation learning
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2020
Cited alongside, same era.
Martinkus, K., Loukas, A., Perraudin, N., and Wattenhofer, R · 2022
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Transformer for graphs: An overview from architecture perspective
Min, E., Chen, R., Bian, Y., Xu, T., Zhao, K., Huang, W., Zhao, P., Huang, J., Ananiadou, S., and Rong, Y · 2022
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Masked autoencoders for point cloud self-supervised learning
Pang, Y., Wang, W., Tay, F. E., Liu, W., Tian, Y., and Yuan, L · 2022
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Graph transplant: Node saliency-guided graph mixup with local structure preservation
Park, J., Shim, H., and Yang, E · 2022
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Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Peng, X., Luo, S., Guan, J., Xie, Q., Peng, J., and Ma, J · 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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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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Digress: Discrete denoising diffusion for graph generation
Vignac, C., Krawczuk, I., Siraudin, A., Wang, B., Cevher, V., and Frossard, P · 2022
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Molecular contrastive learning of representations via graph neural networks
Wang, Y., Wang, J., Cao, Z., and Barati Farimani, A · 2022
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Graphmixup: Improving class-imbalanced node classification by reinforcement mixup and self-supervised context prediction
Wu, L., Xia, J., Gao, Z., et al · 2022
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Simgrace: A simple framework for graph contrastive learning without data augmentation
Xia, J., Wu, L., Chen, J., Hu, B., and Li, S. Z · 2022
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Self-supervised learning of graph neural networks: A unified review
Xie, Y., Xu, Z., Zhang, J., Wang, Z., and Ji, S · 2022
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Point-bert: Pre-training 3d point cloud transformers with masked point modeling
Yu, X., Tang, L., Rao, Y., et al · 2022
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Multi-level cross-view contrastive learning for knowledge-aware recommender system
Zou, D., Wei, W., Mao, X.-L., et al · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Scaling vision transformers to 22 billion parameters
Dehghani, M., Djolonga, J., Mustafa, B., Padlewski, P., Heek, J., Gilmer, J., Steiner, A. P., et al · 2023
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Kw-design: Pushing the limit of protein deign via knowledge refinement
Gao, Z., Tan, C., Chen, X., Zhang, Y., Xia, J., Li, S., and Li, S. Z · 2023
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Interpolating graph pair to regularize graph classification
Guo, H. and Mao, Y · 2023
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Autoregressive diffusion model for graph generation
Kong, L., Cui, J., Sun, H., Zhuang, Y., Prakash, B. A., and Zhang, C · 2023
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Target-aware molecular graph generation
Tan, C., Gao, Z., and Li, S. Z · 2023
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Heterogeneous graph masked autoencoders
Tian, Y., Dong, K., Zhang, C., Zhang, C., and Chawla, N. V · 2023
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Mole-bert: Rethinking pre-training graph neural networks for molecules
Xia, J., Zhao, C., Hu, B., Gao, Z., Tan, C., Liu, Y., Li, S., and Li, S. Z · 2023
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Mixupexplainer: Generalizing explanations for graph neural networks with data augmentation
Zhang, J., Luo, D., and Wei, H · 2023
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Rimeshgnn: A rotation-invariant graph neural network for mesh classification
Shakibajahromi, B., Kim, E., and Breen, D. E · 2024
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