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While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored.
Pac learning from positive statistical queries
Denis, F · 1998
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Positive and unlabeled examples help learning
De Comité, F., Denis, F., Gilleron, R., and Letouzey, F · 1999
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Learning from positive and unlabeled examples
Letouzey, F., Denis, F., and Gilleron, R · 2000
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Partially supervised classification of text documents
Liu, B., Lee, W. S., Yu, P. S., and Li, X · 2002
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Learning to classify texts using positive and unlabeled data
Li, X. and Liu, B · 2003
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Learning from positive and unlabeled examples
Denis, F., Gilleron, R., and Letouzey, F · 2005
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Learning classifiers from only positive and unlabeled data
Elkan, C. and Noto, K · 2008
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Semi-supervised novelty detection
Blanchard, G., Lee, G., and Scott, C · 2010
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Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., and Handy, G · 2013
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Semi-supervised learning of class balance under class-prior change by distribution matching
Du Plessis, M. C. and Sugiyama, M · 2014
Earlier work this paper cites.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2015
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A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Scott, C · 2015
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Nonparametric semi-supervised learning of class proportions
Jain, S., White, M., Trosset, M. W., and Radivojac, P · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Classifying networked text data with positive and unlabeled examples
Li, M., Pan, S., Zhang, Y., and Cai, X · 2016
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Theoretical comparisons of positive-unlabeled learning against positive-negative learning
Niu, G., Du Plessis, M. C., Sakai, T., Ma, Y., and Sugiyama, M · 2016
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Hyperparameter optimization with approximate gradient
Pedregosa, F · 2016
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Mixture proportion estimation via kernel embeddings of distributions
Ramaswamy, H., Scott, C., and Tewari, A · 2016
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Positive-unlabeled learning with non-negative risk estimator
Kiryo, R., Niu, G., Du Plessis, M. C., and Sugiyama, M · 2017
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
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Pu-lp: A novel approach for positive and unlabeled learning by label propagation
Ma, S. and Zhang, R · 2017
Cited alongside, same era.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Gasteiger, J., Bojchevski, A., and Günnemann, S · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
Cited alongside, same era.
Heterogeneous graph neural network
Zhang, C., Song, D., Huang, C., Swami, A., and Chawla, N. V · 2019
Cited alongside, same era.
Learning graph neural networks with positive and unlabeled nodes
Wu, M., Pan, S., Du, L., and Zhu, X · 2021
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Accurate graph-based pu learning without class prior
Yoo, J., Kim, J., Yoon, H., Kim, G., Jang, C., and Kang, U · 2021
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Graph neural networks with heterophily
Zhu, J., Rossi, R. A., Rao, A., Mai, T., Lipka, N., Ahmed, N. K., and Koutra, D · 2021
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Characterizing the influence of graph elements
Chen, Z., Li, P., Liu, H., and Hong, P · 2022
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Finding global homophily in graph neural networks when meeting heterophily
Li, X., Zhu, R., Cheng, Y., Shan, C., Luo, S., Li, D., and Qian, W · 2022
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Revisiting heterophily for graph neural networks
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Learning from positive and unlabeled data: A survey
Bekker, J. and Davis, J · 2020
Cited alongside, same era.
Adaptive universal generalized pagerank graph neural network
Chien, E., Peng, J., Li, P., and Milenkovic, O · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
Cited alongside, same era.
Dedpul: Difference-of-estimated-densities-based positive-unlabeled learning
Ivanov, D · 2020
Cited alongside, same era.
Geom-gcn: Geometric graph convolutional networks
Pei, H., Wei, B., Chang, K. C.-C., Lei, Y., and Yang, B · 2020
Cited alongside, same era.
Unifying graph convolutional neural networks and label propagation
Wang, H. and Leskovec, J · 2020
Cited alongside, same era.
Rethinking class-prior estimation for positive-unlabeled learning
Yao, Y., Liu, T., Han, B., Gong, M., Niu, G., Sugiyama, M., and Tao, D · 2020
Cited alongside, same era.
Luan, S., Hua, C., Lu, Q., Zhu, J., Zhao, M., Zhang, S., Chang, X.-W., and Precup, D · 2022
Later among the works it cites.
Handling distribution shifts on graphs: An invariance perspective
Wu, Q., Zhang, H., Yan, J., and Wipf, D · 2022
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Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks
Yan, Y., Hashemi, M., Swersky, K., Yang, Y., and Koutra, D · 2022
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A holistic view of label noise transition matrix in deep learning and beyond
Yong, L., Pi, R., Zhang, W., Xia, X., Gao, J., Zhou, X., Liu, T., and Han, B · 2022
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How does heterophily impact the robustness of graph neural networks? theoretical connections and practical implications
Zhu, J., Jin, J., Loveland, D., Schaub, M. T., and Koutra, D · 2022
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Revisiting the role of heterophily in graph representation learning: An edge classification perspective
Huang, J., Li, P., Huang, R., Chen, N., and Zhang, A · 2023
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Characterizing graph datasets for node classification: Homophily-heterophily dichotomy and beyond
Platonov, O., Kuznedelev, D., Babenko, A., and Prokhorenkova, L · 2023
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Everything is connected: Graph neural networks
Veličković, P · 2023
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Making binary classification from multiple unlabeled datasets almost free of supervision
Wu, Y., Xia, X., Yu, J., Han, B., Niu, G., Sugiyama, M., and Liu, T · 2023
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Coreset selection with prioritized multiple objectives
Xia, X., Liu, J., Zhang, S., Wu, Q., and Liu, T · 2023
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Beyond myopia: Learning from positive and unlabeled data through holistic predictive trends
Xinrui, W., Geng, C., Li, S.-Y., Chen, S., et al · 2023
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Positive-unlabeled node classification with structure-aware graph learning
Yang, H., Zhang, Y., Yao, Q., and Kwok, J · 2023
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Mixture proportion estimation beyond irreducibility
Zhu, Y., Fjeldsted, A., Holland, D., Landon, G., Lintereur, A., and Scott, C · 2023
Later among the works it cites.
Mitigating label noise on graph via topological sample selection
Wu, Y., Yao, J., Xia, X., Yu, J., Wang, R., Han, B., and Liu, T · 2024
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