Bayesian graph convolutional network with partial observations
Shuhui Luo,
Peilan Liu,
Xulun Ye
Abstract:As a widely studied model in the machine learning and data processing society, graph convolutional network reveals its advantage in non-grid data processing. However, existing graph convolutional networks generally assume that the node features can be fully observed. This may violate the fact that many real applications come with only the pairwise relationships and the corresponding node features are unavailable. In this paper, a novel graph convolutional network model based on Bayesian framework is proposed t… Show more
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