A User Guide to Low-Pass Graph Signal Processing and Its Applications: Tools and Applications

Raksha Ramakrishna, Hoi To Wai, Anna Scaglione

Research output: Contribution to journalArticlepeer-review

Abstract

The notion of graph filters can be used to define generative models for graph data. In fact, the data obtained from many examples of network dynamics may be viewed as the output of a graph filter. With this interpretation, classical signal processing tools, such as frequency analysis, have been successfully applied with analogous interpretation to graph data, generating new insights for data science. What follows is a user guide on a specific class of graph data, where the generating graph filters are low pass; i.e., the filter attenuates contents in the higher graph frequencies while retaining contents in the lower frequencies. Our choice is motivated by the prevalence of low-pass models in application domains such as social networks, financial markets, and power systems. We illustrate how to leverage properties of low-pass graph filters to learn the graph topology and identify its community structure; efficiently represent graph data through sampling; recover missing measurements; and denoise graph data. The low-pass property is also used as the baseline to detect anomalies.

Original languageEnglish (US)
Article number9244171
Pages (from-to)74-85
Number of pages12
JournalIEEE Signal Processing Magazine
Volume37
Issue number6
DOIs
StatePublished - Nov 2020

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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