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This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics.
Marginalized kernels between labeled graphs
Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi · 2003
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Yves Grandvalet and Yoshua Bengio · 2005
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Daniel A Spielman · 2007
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Graph kernels
S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt · 2010
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, and B Aditya Prakash · 2018
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Convolutional kernel networks for graph-structured data
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Asgn: An active semi-supervised graph neural network for molecular property prediction
Zhongkai Hao, Chengqiang Lu, Zhenya Huang, Hao Wang, Zheyuan Hu, Qi Liu, Enhong Chen, and Cheekong Lee · 2020
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kgcn: a graph-based deep learning framework for chemical structures
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Random walk graph neural networks
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang · 2020
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Graph contrastive learning with augmentations
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Semi-supervised graph classification: A hierarchical graph perspective
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Theoretically improving graph neural networks via anonymous walk graph kernels
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Graph contrastive learning automated
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Ghnn: Graph harmonic neural networks for semi-supervised graph-level classification
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Dualgraph: Improving semi-supervised graph classification via dual contrastive learning
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