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Self-supervised learning (SSL) of graph neural networks is emerging as a promising way of leveraging unlabeled data.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds correlation with molecular orbital energies and hydrophobicity
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Citeseer: An automatic citation indexing system
Giles, C. L., Bollacker, K. D., and Lawrence, S · 1998
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The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W · 1999
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Automating the construction of internet portals with machine learning
McCallum, A. K., Nigam, K., Rennie, J., and Seymore, K · 2000
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Distinguishing enzyme structures from non-enzymes without alignments
Dobson, P. D. and Doig, A. J · 2003
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Protein function prediction via graph kernels
Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S. V. N., Smola, A. J., and Kriegel, H.-P · 2005
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Comparison of descriptor spaces for chemical compound retrieval and classification
Wale, N. and Karypis, G · 2006
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
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Efficient graphlet kernels for large graph comparison
Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., and Borgwardt, K · 2009
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A., and Bottou, L · 2010
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Weisfeiler-Lehman graph kernels
Shervashidze, N., Schweitzer, P., Van Leeuwen, E. J., Mehlhorn, K., and Borgwardt, K. M · 2011
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Overlapping community detection in networks: The state-of-the-art and comparative study
Xie, J., Kelley, S., and Szymanski, B. K · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Image-based recommendations on styles and substitutes
McAuley, J., Targett, C., Shi, Q., and Van Den Hengel, A · 2015
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An overview of microsoft academic service (mas) and applications
Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.-J., and Wang, K · 2015
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Deep learning and the information bottleneck principle
Tishby, N. and Zaslavsky, N · 2015
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Deep graph kernels
Yanardag, P. and Vishwanathan, S · 2015
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node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
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Variational graph auto-encoders
Kipf, T. N. and Welling, M · 2016
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Inductive representation learning on large graphs
Hamilton, W., 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.
graph2vec: Learning distributed representations of graphs
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., and Jaiswal, S · 2017
Cited alongside, same era.
MGAE: Marginalized graph autoencoder for graph clustering
Wang, C., Pan, S., Long, G., Zhu, X., and Jiang, J · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., kavukcuoglu, k., Munos, R., and Valko, M · 2020
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Contrastive multi-view representation learning on graphs
Hassani, K. and Khasahmadi, A. H · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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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 · 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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Sub-graph contrast for scalable self-supervised graph representation learning
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Zitnik, M. and Leskovec, J · 2017
Cited alongside, same era.
Sub2Vec: Feature learning for subgraphs
Adhikari, B., Zhang, Y., Ramakrishnan, N., and Prakash, B. A · 2018
Cited alongside, same era.
On the information bottleneck theory of deep learning
Saxe, A. M., Bansal, Y., Dapello, J., Advani, M., Kolchinsky, A., Tracey, B. D., and Cox, D. D · 2018
Cited alongside, same era.
Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
Cited alongside, same era.
Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 2018
Cited alongside, same era.
Noise2Self: Blind denoising by self-supervision
Batson, J. and Royer, L · 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.
Jiao, Y., Xiong, Y., Zhang, J., Zhang, Y., Zhang, T., and Zhu, Y · 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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TUDataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
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Graph representation learning via graphical mutual information maximization
Peng, Z., Huang, W., Luo, M., Zheng, Q., Rong, Y., Xu, T., and Huang, J · 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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Noise2Same: Optimizing a self-supervised bound for image denoising
Xie, Y., Wang, Z., and Ji, S · 2020
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Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
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GraphSAINT: Graph sampling based inductive learning method
Zeng, H., Zhou, H., Srivastava, A., Kannan, R., and Prasanna, V · 2020
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Deep graph contrastive representation learning
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2020
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How to find your friendly neighborhood: Graph attention design with self-supervision
Kim, D. and Oh, A · 2021
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Bootstrapped representation learning on graphs
Thakoor, S., Tallec, C., Azar, M. G., Munos, R., Veličković, P., and Valko, M · 2021
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Theoretical analysis of self-training with deep networks on unlabeled data
Wei, C., Shen, K., Chen, Y., and Ma, T · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S · 2021
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Graph contrastive learning with adaptive augmentation
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2021
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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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