2023
DOI: 10.1007/s13042-023-01982-8
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Enhancing framelet GCNs with generalized p-Laplacian regularization

Zhiqi Shao,
Dai Shi,
Andi Han
et al.

Abstract: Graph neural networks (GNNs) have achieved remarkable results for various graph learning tasks. However, one of the recent challenges for GNNs is to adapt to different types of graph inputs, such as heterophilic graph datasets in which linked nodes are more likely to contain a different class of labels and features. Accordingly, an ideal GNN model should adaptively accommodate all types of graph datasets with different labeling distributions. In this paper, we tackle this challenge by proposing a regularizatio… Show more

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Cited by 3 publications
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