  The Graph Neural Network (GNN) has been widely used for graph data
representation. However, the existing researches only consider the ideal
balanced dataset, and the imbalanced dataset is rarely considered. Traditional
methods such as resampling, reweighting, and synthetic samples that deal with
imbalanced datasets are no longer applicable in GNN. This paper proposes an
ensemble model called Boosting-GNN, which uses GNNs as the base classifiers
during boosting. In Boosting-GNN, higher weights are set for the training
samples that are not correctly classified by the previous classifier, thus
achieving higher classification accuracy and better reliability. Besides,
transfer learning is used to reduce computational cost and increase fitting
ability. Experimental results indicate that the proposed Boosting-GNN model
achieves better performance than GCN, GraphSAGE, GAT, SGC, N-GCN, and most
advanced reweighting and resampling methods on synthetic imbalanced datasets,
with an average performance improvement of 4.5%
