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	This ontology document is licensed under the Creative Commons
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=community+detection">
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  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=community+detection]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for community detection]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/397/Community-Detection-in-Twitter"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/265/Mining-Social-Media-Communities-and-Content"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/251/Communities-in-Social-Media-Reflections-on-Semantics-Intention-and-Influence"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/247/Communities-in-Social-Media-An-Eyepiece-into-Context-User-Intention-and-Influence"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/187/Tracking-influence-and-opinions-in-social-media"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/177/Tracking-Influence-and-Opinions-in-Social-Media"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/555/Community-Detection-in-Twitter"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/429/Mining-Social-Media-Communities-and-Content"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/381/Approximating-the-Community-Structure-of-the-Long-Tail"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/328/Community-Detection-in-Twitter"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/329/Community-Detection-in-Twitter"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/397/Community-Detection-in-Twitter">
  <title><![CDATA[Community Detection in Twitter]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/397/Community-Detection-in-Twitter</link>
  <description><![CDATA[Mohit Kewalramani will defend his MS thesis titled "Community Detection in Twitter".
 
Twitter has evolved into a source of social, political and real time information in addition to being a means of mass-communication and marketing. Monitoring and analyzing information on Twitter can lead to invaluable insights, which might otherwise be hard to get using conventional media resources. An important task in analyzing highly networked information sources like twitter is to identify communities...]]></description>
  <dc:date>2011-05-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/265/Mining-Social-Media-Communities-and-Content">
  <title><![CDATA[Mining Social Media Communities and Content]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/265/Mining-Social-Media-Communities-and-Content</link>
  <description><![CDATA[Ph.D. Dissertation Defense


Social Media is changing the way we find information, share knowledge and
communicate with each other. The important factor contributing to the growth
of these technologies is the ability to easily produce "user-generated
content". Blogs, Twitter, Wikipedia, Flickr and YouTube are just a few
examples of Web 2.0 tools that are drastically changing the Internet landscape
today. These platforms allow users to produce, annotate and share information
with thei...]]></description>
  <dc:date>2008-10-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/251/Communities-in-Social-Media-Reflections-on-Semantics-Intention-and-Influence">
  <title><![CDATA[Communities in Social Media: Reflections on Semantics, Intention and Influence]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/251/Communities-in-Social-Media-Reflections-on-Semantics-Intention-and-Influence</link>
  <description><![CDATA[Communities are central to online social media systems and detecting
their structure and membership is critical for many applications. A
community in real world is represented in a graph as a set of nodes
that are more closely related to one another than the rest of the
network. In social media, a community could be a set of blogs that are
topically related, a group of friends connected via Live Spaces or
even a set of users who share similar tags in their social bookmarks.
Graph struc...]]></description>
  <dc:date>2008-08-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/247/Communities-in-Social-Media-An-Eyepiece-into-Context-User-Intention-and-Influence">
  <title><![CDATA[Communities in Social Media: An Eyepiece into Context, User Intention and Influence]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/247/Communities-in-Social-Media-An-Eyepiece-into-Context-User-Intention-and-Influence</link>
  <description><![CDATA[Communities are central to online social media systems and detecting their structure and membership is critical for many applications. In this talk, I will discuss some of our recent research on both identifying communities and analyzing their content. We leverage the special properties of Social Media data to analyze the communities in an attempt to understand user intentions, context and influence.

 Community detection techniques can be computationally expensive. An approach to reducing ...]]></description>
  <dc:date>2008-06-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/187/Tracking-influence-and-opinions-in-social-media">
  <title><![CDATA[Tracking influence and opinions in social media]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/187/Tracking-influence-and-opinions-in-social-media</link>
  <description><![CDATA[Recently, social media such as forums, wikis and blogs, in
particular, are playing a notable role in influencing the
buying patterns of consumers.  Often a person looks for
opinions, user experiences and reviews on such sources
before purchasing a product.  Detecting influential nodes,
opinion leaders and understanding their role in how people
perceive and adopt a product or service provides a powerful
tool for marketing, advertising and business
intelligence. This requires new algori...]]></description>
  <dc:date>2006-11-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/177/Tracking-Influence-and-Opinions-in-Social-Media">
  <title><![CDATA[Tracking Influence and Opinions in Social Media]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/177/Tracking-Influence-and-Opinions-in-Social-Media</link>
  <description><![CDATA[Social Media such as blogs, wikis, formus and user-generated content
sites like flickr,
delicious and youtube have become both a source of information and
entertainment.
The size of audience that these sites currently yield is already rivaling
traditional main stream media sources like television, newspapers and
magazines.
Blogs, especially, have been reported to play a notable role in
influencing the buying patterns of consumers. Often a buyer looks for
opinions, user experiences an...]]></description>
  <dc:date>2006-10-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs</link>
  <description><![CDATA[Topic models are widely used to thematically describe a collection of text documents and have become an important technique for systems that measure document similarity for classification, clustering, segmentation, entity linking, and more.  While they have been applied to some non-text domains, their use for semi-structured graph data, such as RDF, has been less explored.  We present a framework for applying topic modeling to RDF graph data and describe how it can be used in a number of link...]]></description>
  <dc:date>2015-10-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/555/Community-Detection-in-Twitter">
  <title><![CDATA[Community Detection in Twitter]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/555/Community-Detection-in-Twitter</link>
  <description><![CDATA[Twitter has recently evolved into a source of social, political and real time information in addition to being a means of mass-communication and marketing. Monitoring
and analyzing information on Twitter can lead to invaluable insights, which might otherwise
be hard to get using conventional media resources. An important task in analyzing highly networked information sources like twitter is to identify communities that are formed. A
community on twitter can be defined as a set of users tha...]]></description>
  <dc:date>2011-05-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/429/Mining-Social-Media-Communities-and-Content">
  <title><![CDATA[Mining Social Media Communities and Content]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/429/Mining-Social-Media-Communities-and-Content</link>
  <description><![CDATA[Social Media is changing the way people find information, share
knowledge and communicate with each other. The important factor
contributing to the growth of these technologies is the ability to
easily produce “user-generated content”. Blogs, Twitter, Wikipedia,
Flickr and YouTube are just a few examples of Web 2.0 tools that are
drastically changing the Internet landscape today. These platforms
allow users to produce and annotate content and more importantly,
empower them to share...]]></description>
  <dc:date>2008-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/381/Approximating-the-Community-Structure-of-the-Long-Tail">
  <title><![CDATA[Approximating the Community Structure of the Long Tail]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/381/Approximating-the-Community-Structure-of-the-Long-Tail</link>
  <description><![CDATA[In many social media applications, a small fraction of the members are
highly linked while most are sparsely connected to the network. Such a
skewed distribution is sometimes referred to as the "long
tail". Popular applications like meme trackers and content aggregators
mine for information from only the popular blogs located at the head
of this curve. On the other hand, the long tail contains large volumes
of interesting information and niches. The question we address in this
work is ...]]></description>
  <dc:date>2008-03-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/328/Community-Detection-in-Twitter">
  <title><![CDATA[Community Detection in Twitter]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/328/Community-Detection-in-Twitter</link>
  <dc:date>1999-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/329/Community-Detection-in-Twitter">
  <title><![CDATA[Community Detection in Twitter]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/329/Community-Detection-in-Twitter</link>
  <dc:date>1999-11-30</dc:date>
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