用信息论方法自动选择图扩散小波的尺度，提升图神经网络性能。
InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets
- 基于信息论原理自动确定图扩散的尺度，无需人工设定。
- 在图分类任务中，新方法使基于小波的GNN模型准确率显著提升。
- 适合研究图神经网络与小波分析结合的学者参考。
扩散小波通过将图扩散算子提升到不同幂次（即扩散尺度）来提取图信号在多尺度下的信息。传统方法通常采用二进制尺度（如 $2^j$）。本文提出一种新的无监督方法，基于信息论思想自适应选择扩散尺度，并将其融入基于小波的图神经网络（类几何散射变换架构），通过图分类实验验证了其有效性。
原文摘要 · Abstract (English)
Diffusion wavelets extract information from graph signals at different scales of resolution by utilizing graph diffusion operators raised to various powers, known as diffusion scales. Traditionally, these scales are chosen to be dyadic integers, $2^j$. Here, we propose a novel, unsupervised method for selecting the diffusion scales based on ideas from information theory. We then show that our method can be incorporated into wavelet-based GNNs, which are modeled after the geometric scattering transform, via graph classification experiments.
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