GCN-SynDCL: Imbalanced Node Classification via GCN and Synthetic Node Generation Using Dynamic Curriculum Learning

The class imbalance problem is a prevalent challenge in the domain of network science for node classification tasks, often leading to biased models and poor performance on minority classes. To address this issue, we propose GCN-SynDCL, a model for imbalanced node classification that utilizes Graph Convolutional Networks (GCN), Multi-Layer Perceptrons (MLP), and synthetic node generation with Dynamic Curriculum Learning (DCL). The feature extraction is performed using GCN as an encoder and MLP as a decoder, where GCN captures both topological and feature-level information, while the MLP reconstructs node features to ensure semantic consistency. These embeddings are then used to generate synthetic nodes for minority classes, which are progressively integrated into training via DCL to improve representation and reduce overfitting. Next, synthetic nodes are integrated into the graph using adaptive k-nearest neighbor (k-NN) with cosine similarity to form structurally coherent edges. Finally, a GraphSAGE classifier is trained on the augmented graph, leveraging both original and synthetic nodes for improved classification performance across all classes, particularly the minority ones. Furthermore, comprehensive studies on benchmark datasets demonstrate that GCN-SynDCL consistently outperforms contemporary methods in handling the class imbalance problem. Our approach offers a flexible and robust solution to imbalanced node classification with broad applicability.