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Khasawneh-Lab/PH_complex_networks: PHN

Audun Myers; Firas Khasawneh; Elizabeth Munch


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  <identifier identifierType="DOI">10.5281/zenodo.3731096</identifier>
  <creators>
    <creator>
      <creatorName>Audun Myers</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6268-9227</nameIdentifier>
      <affiliation>Michigan State University</affiliation>
    </creator>
    <creator>
      <creatorName>Firas Khasawneh</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7817-7445</nameIdentifier>
      <affiliation>Michigan State University</affiliation>
    </creator>
    <creator>
      <creatorName>Elizabeth Munch</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9459-9493</nameIdentifier>
      <affiliation>Michigan State University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Khasawneh-Lab/PH_complex_networks: PHN</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-03-27</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3731096</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/Khasawneh-Lab/PH_complex_networks/tree/V1.0.0</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3731095</relatedIdentifier>
  </relatedIdentifiers>
  <version>V1.0.0</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Persistent Homology of Networks (PHN) is a python package for the functions developed in &amp;quot;Persistent Homology of Complex Networks for Dynamic State Detection.&amp;quot; These functions use a time series to develop a complex network, which can be analyzed to detect dynamic state changes as well as complexity changes in the time series. Additionally, complex networks allow for a unique view into the shape of high dimension embedding that could not normally be visualized.&lt;/p&gt;</description>
  </descriptions>
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