Enhanced Structural Information Over-sampling with Graph Neural Networks for Imbalanced Node Classification on Graphs
Zhen Tan, Jie Li, Yuhan Bao, Yuhan Bao · 2023
Graph neural networks play a crucial role in various fields of artificial intelligence, but graph structures themselves have the problem of imbalanced nodes. Currently, many oversampling methods have been proposed to address this problem. However, the combination of Graph Neural Networks(GNNs) and over-sampling methods still needs further exploration, for example, global information is always ignored. To address this issue, this paper proposes a novelty framework called Enhanced Structural Information Over-sampling (ESIO-GCN), which combines GNNs with enhanced global structural information and deep over-sampling. Over-sampling is carried out in the middle embedding layer of ESIO-GCN. In addition, this paper also explores different over-sampling strategies. We evaluate ESIO-GCN on several standard semi-supervised node classification datasets and achieved significant improvements on all datasets.