Graph Structure Refinement Neural Networks for Robust Node Classification in API-based Systems
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Abstract
Graph Neural Networks (GNNs) are widely used for node classification on graph-structured data, including graphs induced from API-based power systems. However, the effectiveness of existing GNNs strongly depends on the quality of the underlying graph structure, which is often noisy or suboptimal in practice. In this paper, we propose Graph Structure Refinement Neural Network (GSR-GNN), a novel framework that learns task-aware edge importance weights to refine graph structures. GSR-GNN employs a differentiable structure refinement module and a refined message passing scheme to enable more robust information aggregation, together with a structure-preserving loss to maintain essential topological properties. Experimental results on benchmark datasets demonstrate that GSR-GNN consistently outperforms state-of-the-art methods.
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