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Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-based learning problem, such as credit risk assessment in financial networks and fake news detection in social networks.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
Earlier work this paper cites.
Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitriy Pyrkin, Sergei Popov, and Artem Babenko · 2004
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Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitriy Pyrkin, Sergei Popov, and Artem Babenko · 2004
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Inductive representation learning on large graphs
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Semi-supervised classification with graph convolutional networks
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Fake news detection on social media: A data mining perspective
Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu · 2017
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A dynamic approach merging network theory and credit risk techniques to assess systemic risk in financial networks
Daniele Petrone and Vito Latora · 2018
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Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Matthew Sotoudeh and A Thakur · 2019
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Do language models have beliefs? methods for detecting, updating, and visualizing model beliefs
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Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, and Christopher D Manning · 2021
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Adaptive label smoothing to regularize large-scale graph training
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Geometric graph representation learning via maximizing rate reduction
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Memory-based model editing at scale
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Weight perturbation can help fairness under distribution shift
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