Shape-aware Graph Spectral Learning

Junjie Xu, Enyan Dai, Dongsheng Luo, Xiang Zhang, Suhang Wang

Abstract: Spectral Graph Neural Networks (GNNs) are gaining attention for their ability to surpass the limitations of message-passing GNNs. They rely on supervision from downstream tasks to learn spectral filters that capture useful graph frequency information. However, some works empirically show that the preferred graph frequency is related to the graph homophily level. The relationship between graph frequency and graph homophily level has not been systematically analyzed and explored in existing spectral GNNs. To mitigate this gap, we conduct theoretical and empirical analyses revealing a positive correlation between low-frequency importance and the homophily ratio, and a negative correlation between high-frequency importance and the homophily ratio. Motivated by this, we propose shape-aware regularization on a Newton Interpolation-based spectral filter that can (i) learn an arbitrary polynomial spectral filter; and (ii) incorporate prior knowledge about the desired shape of the corresponding homophily level. Comprehensive experiments demonstrate that NewtonNet can achieve graph spectral filters with desired shapes and superior performance on both homophilous and heterophilous datasets. Our code is available at https://github.com/junjie-xu/NewtonNet.