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cs.LG2025
Understanding and Mitigating Under-Confidence in GNNs from the Final Layer
Jincheng Huang, Jie Xu, Xiaoshuang Shi +3
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness on graph-based tasks. However, their predictive confidence is often miscalibrated, typically exhibiting unde…
cs.LG2024★ 1 cited
Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks
Jincheng Huang, Yujie Mo, Xiaoshuang Shi +2
The message-passing mechanism of graph convolutional networks (i.e., GCNs) enables label information to reach more unlabeled neighbors, thereby increasing the utilization of labels…