Comparative Analysis of Bi-View Embedding Fusion and Standard Graph Neural Networks for Few-Shot Node Classification on Sparse

Abstract

Abstract: Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in three key aspects: 1) suboptimal graph topology construction due to fixed or heuristic-based edge definitions, 2) inefficient propagation of discriminative node features across heterogeneous regions, and 3) inadequate fusion of spatially correlated and spectrally discriminative patterns. To overcome these challenges, we propose a Spatial-Spectral Contrastive Graph Neural Network (SSCGNN), which introduces th

Research Question

How does bi-view embedding fusion compare to standard graph neural networks in few-shot node classification tasks on sparse knowledge graphs?

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