用博弈论方法精准删边，让图神经网络更快更准。
Shapley-Value-Based Graph Sparsification for GNN Inference
- 基于博弈论的边重要性评分，可正可负，更全面评估影响。
- 删减超半数边后模型精度下降不足1%，显著降低计算开销。
- 适合追求高效高解释性的图神经网络部署场景。
图稀疏化是提升图神经网络推理效率的关键技术，通过移除对预测影响最小的边来实现。现有GNN可解释性方法生成局部重要性评分，可聚合为全局评分用于稀疏化，但多数仅输出非负分数，限制了其在稀疏化中的应用。相比之下，基于Shapley值的方法能为节点预测分配正负贡献，理论上更公平且稳健，通过评估大量子图集合实现重要性分配。与基于梯度或扰动的解释器不同，Shapley值支持更优的剪枝策略，既能保留关键边，又能剔除误导性或对抗性连接。本方法证明，基于Shapley值的图稀疏化在显著降低图复杂度的同时保持预测性能，同时提升GNN推理的可解释性与效率。
原文摘要 · Abstract (English)
Graph sparsification is a key technique for improving inference efficiency in Graph Neural Networks by removing edges with minimal impact on predictions. GNN explainability methods generate local importance scores, which can be aggregated into global scores for graph sparsification. However, many explainability methods produce only non-negative scores, limiting their applicability for sparsification. In contrast, Shapley value based methods assign both positive and negative contributions to node predictions, offering a theoretically robust and fair allocation of importance by evaluating many subsets of graphs. Unlike gradient-based or perturbation-based explainers, Shapley values enable better pruning strategies that preserve influential edges while removing misleading or adversarial connections. Our approach shows that Shapley value-based graph sparsification maintains predictive performance while significantly reducing graph complexity, enhancing both interpretability and efficiency in GNN inference.
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