A User Guide to Low-Pass Graph Signal Processing and its Applications
- URL: http://arxiv.org/abs/2008.01305v1
- Date: Tue, 4 Aug 2020 03:27:17 GMT
- Title: A User Guide to Low-Pass Graph Signal Processing and its Applications
- Authors: Raksha Ramakrishna, Hoi-To Wai, Anna Scaglione
- Abstract summary: We show how to leverage properties of low-pass graph filters to learn the graph topology or identify its community structure.
We illustrate how to represent graph data through sampling, recover missing measurements, and de-noise graph data.
- Score: 31.90359683602266
- License: http://creativecommons.org/licenses/by/4.0/
- 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 or identify its community structure;
efficiently represent graph data through sampling, recover missing
measurements, and de-noise graph data; the low-pass property is also used as
the baseline to detect anomalies.
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