InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data

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, the diffusion scales are chosen to be dyadic integers, $\mathbf{2^j}$. 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 via graph classification experiments.

@article{johnson2025_2504.08802,
  title={ InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data },
  author={ David R. Johnson and Smita Krishnaswamy and Michael Perlmutter },
  journal={arXiv preprint arXiv:2504.08802},
  year={ 2025 }
}