A Kernel Propagation-Based Graph Convolutional Network Imbalanced Node Classification Model on Graph Data

As an important research topic in graph learning, the performance of node classification has been improved with the development of some new methods, among which graph neural networks (GNNs) have achieved state-of-the-art node classification performance. However, the existing GNN-based methods mainly address the classification problem with balanced distribution of node samples. However, many real application scenarios of graph data usually have a highly skewed class distribution, i.e., the majority classes occupy most of the samples while the minority classes contain only a few samples. When the nodes exhibit an imbalanced class distribution, existing GNN-based methods favor the majority class and under-represent the minority class. Therefore, we propose a novel Kernel Propagation-based model for Imbalanced Node Classification in Graph Convolutional Networks (KINC-GCN). First, we introduce a kernel propagation method as a preprocessing step to exploit higher-order structural features. The node features are enhanced by concatenating the higher-order structural feature matrix with the node feature matrix. Node embeddings are obtained from the enhanced feature and adjacency matrices by a two-layer GCN, and then a self-optimizing cluster analysis and graph reconstruction module are introduced. The self-optimizing cluster analysis module performs cluster analysis on the node embeddings to enhance the representativeness of the node embeddings. The graph reconstruction module uses an inner product decoder to reconstruct the graph structure and minimize the differences between the reconstructed graph and the original graph. The effectiveness of KINC-GCN in node classification is demonstrated by experiments on three real-world imbalanced graph datasets.