## 2 Related Work  
### Imbalanced Node Classification  
Most existing GNN models are based on the assumption that node samples from different classes are balanced [2]. However, in many real-world graphs, there is a problem of class imbalance. Training a graph neural network (GNN) classifier directly with the original data can be problematic in scenarios where there is an imbalance between the classes. This is because the GNN may not adequately represent samples from minority classes, resulting in poor performance.  
Currently, there have been some studies focused on solving the problem of unbalanced node classification. For example, [PERSON] et al. [3] proposed a novel graph convolutional network that employs two regularization methods to handle class imbalances in graph data. It achieves balanced training across different classes by training unlabeled nodes to have a data distribution similar to well-trained nodes. However, the limitation of this algorithm is its difficulty in generalizing to large-scale graphs. Additionally, [PERSON] et al. [2] perform oversampling by generating nodes and edges to achieve class balance. However, this approach encounters challenges in edge generation, such as high time complexity and overlooking intricate relationships and feature interactions among nodes, potentially compromising the quality of generated edges. [PERSON] et al. [4] introduced GraphMixup, which establishes a distinct semantic space for conducting semantic feature mixing at the semantic level, taking into account both local and global information in the graph structure. Nevertheless, GraphMixup interpolates between data from two distinct graphs, which may blur the original data features and introduce noise and inconsistencies. The ImGAGN [5] synthesizes mini-nodes by interpolating the generated weight matrix into features between the entire mini-nodes and then performing classification by connecting the synthesized nodes with the original mini-nodes. The drawback of this approach is that it may lead to information loss. Subsequently, [PERSON] [6] proposed GraphENS, a novel data augmentation model that synthesizes minor nodes using all nodes, leading to superior performance, particularly when the graph has a small number of minor classes. Nonetheless, the GraphENS algorithm updates the representation of the target node by incorporating information from neighboring nodes, which could be noisy or erroneous, potentially compromising the quality of embeddings. Despite adopting an adaptive neighbor selection mechanism to mitigate this sensitivity, it cannot fully eliminate it and necessitates preprocessing of the dataset, resulting in potential information loss. [PERSON] et al. [7] introduced GNNCL, a graph neural network framework with curriculum learning that employs adaptive graph oversampling to augment nodes and edges in the graph, enhancing the representation quality of minority class nodes through metric learning and neighbor-based triplet loss. However, due to label imbalance, the GNNCL model often struggles to effectively differentiate and classify minority classes, thereby impacting its accuracy and generalization capability. Furthermore, the GNNCL model heavily relies on structural information from the input graph, and if the input graph is incomplete or contains noise, it can significantly affect the model's performance.  
In summary, existing research on addressing the problem of unbalanced node classification has some drawbacks and limitations, such as inaccurate edge generation during resampling, poor generalization ability, susceptibility to noise, and high requirements for graph structure quality. These limitations may restrict the practical application of these algorithms in real-world scenarios.