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Decentralized Search and the Clustering Paradox in Large Scale Information Networks

Decentralized Search and the Clustering Paradox in Large Scale Information Networks
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Author(s): Weimao Ke (College of Information Science and Technology, Drexel University, USA)
Copyright: 2012
Pages: 18
Source title: Next Generation Search Engines: Advanced Models for Information Retrieval
Source Author(s)/Editor(s): Christophe Jouis (Universite Paris III, France and LIP6-Universite Pierre et Marie Curie, France), Ismail Biskri (Universite du Quebec A Trois Rivieres, Canada), Jean-Gabriel Ganascia (LIP6 and CNRS-Universite Pierre et Marie Curie, France)and Magali Roux (LIP6 and CNRS-Universite Pierre et Marie Curie, France)
DOI: 10.4018/978-1-4666-0330-1.ch002

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Abstract

Amid the rapid growth of information today is the increasing challenge for people to navigate its magnitude. Dynamics and heterogeneity of large information spaces such as the Web raise important questions about information retrieval in these environments. Collection of all information in advance and centralization of IR operations are extremely difficult, if not impossible, because systems are dynamic and information is distributed. The chapter discusses some of the key issues facing classic information retrieval models and presents a decentralized, organic view of information systems pertaining to search in large scale networks. It focuses on the impact of network structure on search performance and discusses a phenomenon we refer to as the Clustering Paradox, in which the topology of interconnected systems imposes a scalability limit.

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