2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2023
FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction
Rui Ding, Jielong Yang, Feng Ji +2
Due to inappropriate sample selection and limited training data, a distribution shift often exists between the training and test sets. This shift can adversely affect the test perf…
cs.LG2023★ 2 cited
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
Feng Ji, See Hian Lee, Hanyang Meng +3
In node classification using graph neural networks (GNNs), a typical model generates logits for different class labels at each node. A softmax layer often outputs a label predictio…
eess.SP2023
Distributional Signals for Node Classification in Graph Neural Networks
Feng Ji, See Hian Lee, Kai Zhao +2
In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP). While it is common in GSP to impose sig…