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IEEE SocialCom 2011, Workshop on Modeling Social Media (MSM 2011)</swrc:booktitle><swrc:publisher><swrc:Organization swrc:name="IEEE Computer Society"/></swrc:publisher><swrc:title>Modeling Location-Based Profiles of Social Image Media using Explorative Pattern Mining</swrc:title><swrc:year>2011</swrc:year><swrc:keywords>2011 data discovery itegpub knowledge mining myown pattern social </swrc:keywords><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Florian Lemmerich"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Martin Atzmueller"/></rdf:_2></rdf:Seq></swrc:author></rdf:Description><rdf:Description rdf:about="http://www.bibsonomy.org/bibtex/28ad9a4592710e41d9d7fb9eba0cee79c/atzmueller"><owl:sameAs rdf:resource="http://www.bibsonomy.org/uri/bibtex/28ad9a4592710e41d9d7fb9eba0cee79c/atzmueller"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#Proceedings"/><swrc:date>Fri Oct 14 23:12:53 CEST 2011</swrc:date><swrc:address>Boston, MA, USA</swrc:address><swrc:publisher><swrc:Organization swrc:name="IEEE Computer Society"/></swrc:publisher><swrc:title>Profile Mining in CVS-Logs and Face-to-Face Contacts for Recommending Software Developers</swrc:title><swrc:volume>Proc. 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However, the tags associated with an image generally are in a random order without any importance or relevance information, which limits the effectiveness of these tags in search and other applications. In this paper, we propose a tag ranking scheme, aiming to automatically rank the tags associated with a given image according to their relevance to the image content. We first estimate initial relevance scores for the tags based on probability density estimation, and then perform a random walk over a tag similarity graph to refine the relevance scores. Experimental results on a 50, 000 Flickr photo collection&lt;/p&gt; &lt;p&gt;show that the proposed tag ranking method is both effective and efficient. 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