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<h1 class="page-header">Community Detection in Social Media</h1>
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<span property="dc:title" content="Community Detection in Social Media" class="rdf-meta element-hidden"></span><span property="sioc:num_replies" content="0" datatype="xsd:integer" class="rdf-meta element-hidden"></span> </header>
<div id="biblio-node"><span class="Z3988" title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.atitle=Community+Detection+in+Social+Media&rft.date=2011"></span><div class="table-responsive">
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<tr><td class="biblio-row-title">Title</td><td>Community Detection in Social Media</td> </tr>
<tr><td class="biblio-row-title">Publication Type</td><td>Journal Article</td> </tr>
<tr><td class="biblio-row-title">Year of Publication</td><td>2011</td> </tr>
<tr><td class="biblio-row-title">Abstract</td><td><p>The proposed survey discusses the topic of community detection in the contextof Social Media. Community detection constitutes a significant tool for the analysisof complex networks by enabling the study of mesoscopic structures that are often associatedwith organizational and functional characteristics of the underlying networks.Community detection has proven to be valuable in a series of domains, e.g. biology, socialsciences, bibliometrics. However, despite the unprecedented scale, complexity andthe dynamic nature of the networks derived from Social Media data, there has onlybeen limited discussion of community detection in this context. More specifically, thereis hardly any discussion on the performance characteristics of community detectionmethods as well as the exploitation of their results in the context of real-world webmining and information retrieval scenarios.To this end, this survey first frames the concept of community and the problem ofcommunity detection in the context of Social Media, and provides a compact classificationof existing algorithms based on their methodological principles. The survey placesspecial emphasis on the performance of existing methods in terms of computationalcomplexity and memory requirements. It presents both a theoretical and an experimentalcomparative discussion of several popular methods. In addition, it discussesthe possibility for incremental application of the methods and proposes five strategiesfor scaling community detection to real-world networks of huge scales. Finally, the surveydeals with the interpretation and exploitation of community detection results inthe context of intelligent web applications and services.</p>
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<p><a href="http://www.auth.gr" target="_blank">Aristotle University of Thessaloniki</a></p>
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