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  <titleInfo>
    <title>Statistical and machine learning approaches for network analysis</title>
  </titleInfo>
  <name type="personal">
    <namePart>Dehmer, Matthias</namePart>
    <namePart type="date">1968-</namePart>
  </name>
  <name type="personal">
    <namePart>Basak, Subhash C.</namePart>
    <namePart type="date">1945-</namePart>
  </name>
  <name type="corporate">
    <namePart>ebrary, Inc</namePart>
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  <typeOfResource>text</typeOfResource>
  <genre authority="marc">bibliography</genre>
  <genre authority="local">Electronic books.</genre>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">nju</placeTerm>
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    <place>
      <placeTerm type="text">Hoboken, N.J</placeTerm>
    </place>
    <publisher>Wiley</publisher>
    <dateIssued>2012</dateIssued>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">electronic</form>
    <form authority="gmd">electronic resource</form>
    <extent>xii, 331 p. : ill.</extent>
  </physicalDescription>
  <abstract>"This book explores novel graph classes and presents novel methods to classify networks. It particularly addresses the following problems: exploration of novel graph classes and their relationships among each other; existing and classical methods to analyze networks; novel graph similarity and graph classification techniques based on machine learning methods; and applications of graph classification and graph mining. Key topics are addressed in depth including the mathematical definition of novel graph classes, i.e. generalized trees and directed universal hierarchical graphs, and the application areas in which to apply graph classes to practical problems in computational biology, computer science, mathematics, mathematical psychology, etc"--</abstract>
  <note type="statement of responsibility">edited by Matthias Dehmer, Subhash C. Basak.</note>
  <note>Includes bibliographical references and index.</note>
  <note>Electronic reproduction. Palo Alto, Calif. : ebrary, 2011. Available via World Wide Web. Access may be limited to ebrary affiliated libraries.</note>
  <subject authority="lcsh">
    <topic>Research</topic>
    <topic>Statistical methods</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Machine theory</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Communication</topic>
    <topic>Network analysis</topic>
    <topic>Graphic methods</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Information science</topic>
    <topic>Statistical methods</topic>
  </subject>
  <classification authority="lcc">Q180.55.S7 S73 2012eb</classification>
  <classification authority="ddc" edition="23">511/.5</classification>
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