论文标题
谱图神经网络何时在节点分类中失败？
When Does A Spectral Graph Neural Network Fail in Node Classification?
论文作者
论文摘要
光谱图神经网络（GNN）具有各种图形过滤器，由于它们在图形学习问题中的表现有希望，因此获得了广泛的肯定。但是，众所周知，GNN并不总是表现良好。尽管图形过滤器为模型解释提供了理论基础，但尚不清楚光谱GNN何时会失败。在本文中，侧重于节点分类问题，我们通过研究其预测误差对频谱GNNS性能进行了理论分析。借助图指标，包括我们提出的同质程度和响应效率，我们对图形结构，节点标签和图形过滤器之间的复杂关系建立了全面的理解。我们指出，标签差异响应效率低的图形过滤器容易失败。为了提高GNNS的性能，我们使用数据驱动的滤波器库为过滤器设计提供了一种更好的滤波设计策略，并提出了简单的模型以进行经验验证。实验结果表明与我们的理论结果一致，并支持我们的策略。
Spectral Graph Neural Networks (GNNs) with various graph filters have received extensive affirmation due to their promising performance in graph learning problems. However, it is known that GNNs do not always perform well. Although graph filters provide theoretical foundations for model explanations, it is unclear when a spectral GNN will fail. In this paper, focusing on node classification problems, we conduct a theoretical analysis of spectral GNNs performance by investigating their prediction error. With the aid of graph indicators including homophily degree and response efficiency we proposed, we establish a comprehensive understanding of complex relationships between graph structure, node labels, and graph filters. We indicate that graph filters with low response efficiency on label difference are prone to fail. To enhance GNNs performance, we provide a provably better strategy for filter design from our theoretical analysis - using data-driven filter banks, and propose simple models for empirical validation. Experimental results show consistency with our theoretical results and support our strategy.
