Decentralized Search and the Clustering Paradox in Large Scale Information Networks

Decentralized Search and the Clustering Paradox in Large Scale Information Networks

Weimao Ke (College of Information Science and Technology, Drexel University, USA)
DOI: 10.4018/978-1-4666-0330-1.ch002
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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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Related challenges for search in distributed settings have been studied in areas of distributed (federated) IR, peer-to-peer networks, multi-agent systems, and complex networks (Callan, 2000; Crespo & Garcia-Molina, 2005; Yu & Singh, 2003; Kleinberg, 2006). In peer-to-peer information retrieval research, for example, problems regarding the applicability of federated IR models in fully distributed environments and scalability of various P2P search models were scrutinized (Zarko & Silvestri, 2007).

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