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    <identifier>10.57760/sciencedb.j00133.00226</identifier>
    <datestamp>2023-03-29T08:45:02Z</datestamp>
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  <dc:date>2023-03-29</dc:date>
  <dc:title>Experimental Data for Community Detection on Node and Edge Importance Analysis</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.j00133.00226</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>In order to verify the effectiveness and efficiency of the community detection algorithm on node and edge importance analysis, experiments are carried out on two types of datasets:(1) The synthetic network is generated by the LFR benchmark, and the submitted file contains the algorithm code, parameter meaning, and description information.(2) The real-world network is selected from the Stanford Network Analysis Project SNAP (http://snap.stanford.edu/data/index.html#socnets), and the submitted file contains the preprocessed network data and description information.</dc:description>
  <dc:subject>synthetic network; real-world network; experimental results</dc:subject>
  <dc:creator>ShinyGao</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/publicdomain/zero/1.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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