HG-SCC: A Subgraph-Aware Convolutional Few-Shot Classification Method on Heterogeneous Graphs

Few-shot classification is increasingly relevant in emerging applications, such as university course classification in intelligent education systems. University course classification helps students acquire specific skills, comprehend course purposes, and assists departments in defining training goals. However, classifying frontier courses presents challenges due to the absence of labels and descriptions. Few-shot learning addresses this by acquiring meta-knowledge. Heterogeneous graphs (HGs), rich in semantic information, introduce complexities that make few-shot particularly challenging. Addressing this problem, we propose a subgraph-aware convolutional few-shot classification method on HGs (HG-SCC). We first formalize the subgraph sampling strategy for HGs and different views under meta-paths. Then, the layer number adaptive spectral-based graph convolution is designed for personalized node embedding. Furthermore, a high-order convolution operation with classes as nodes is designed to increase the class representation coverage. Modeling subgraph centrality, combined with node features, captures structural information, improving awareness of each sampled subgraph, thus alleviating sparsity in new class labels and enhancing classification accuracy. Euclidean distance-based and task-affected cosine similarity-based classifiers under different meta-paths are proposed, with stacking introduced to blend multiple classifiers based on subgraph features. Experimental results show that our method has high performance in course classification and also outperforms state-of-the-art methods on benchmark datasets.