Related papers: Stratified Graph Spectra

Stratified Graph Spectra

URL: http://arxiv.org/abs/2201.03696v1
Date: Mon, 10 Jan 2022 23:35:13 GMT
Title: Stratified Graph Spectra
Authors: Fanchao Meng, Mark Orr, Samarth Swarup
Abstract summary: This paper seeks a generalized transformation decoding the magnitudes of eigencomponents from vector-valued signals. Several attempts are explored, and it is found that performing the transformation at hierarchical levels of adjacency help profile the spectral characteristics of signals more insightfully.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In classic graph signal processing, given a real-valued graph signal, its graph Fourier transform is typically defined as the series of inner products between the signal and each eigenvector of the graph Laplacian. Unfortunately, this definition is not mathematically valid in the cases of vector-valued graph signals which however are typical operands in the state-of-the-art graph learning modeling and analyses. Seeking a generalized transformation decoding the magnitudes of eigencomponents from vector-valued signals is thus the main objective of this paper. Several attempts are explored, and also it is found that performing the transformation at hierarchical levels of adjacency help profile the spectral characteristics of signals more insightfully. The proposed methods are introduced as a new tool assisting on diagnosing and profiling behaviors of graph learning models.

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