The authors prove PAC-Bayesian generalization bounds for one- and two-layer graph convolutional networks in inductive node classification, where new nodes arrive after training.
Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, and Erik Cambria
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PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification
The authors prove PAC-Bayesian generalization bounds for one- and two-layer graph convolutional networks in inductive node classification, where new nodes arrive after training.