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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
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  <description><![CDATA[UMBC ebiquity RSS Tag Search for folksonomy]]></description>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics">
  <title><![CDATA[Social media analytics]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics</link>
  <description><![CDATA[This week's ebiquity meeting will focus on social media and two research efforts that are part of our Relief Social Media project.

Mohit Kewalramani will present the topic that he is addressing in his MS research.  An important task in analyzing highly networked information sources like Twitter is to identify communities that are formed. A community can be defined as a group of nodes that have more links within the set than outside it. We plan to present a technique for detecting communiti...]]></description>
  <dc:date>2010-10-12</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/406/Detecting-Commmunities-via-Simultaneous-Clustering-of-Graphs-and-Folksonomies">
  <title><![CDATA[Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/406/Detecting-Commmunities-via-Simultaneous-Clustering-of-Graphs-and-Folksonomies</link>
  <description><![CDATA[We present a simple technique for detecting communities by utilizing both the link structure and folksonomy (or tag) information that is readily available in most social media systems. A simple way to describe our approach is by defining a community as a set of nodes in a graph that link more frequently within this set than outside it, and they share similar tags. Our technique is based on the Normalized Cut (NCut) algorithm and can be easily and efficiently implemented. We validate our metho...]]></description>
  <dc:date>2008-08-24</dc:date>
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