Adaptive Peer-to-Peer Social Networks for Distributed Content-Based Web Search

Adaptive Peer-to-Peer Social Networks for Distributed Content-Based Web Search

Le-Shin Wu (Indiana University, USA), Ruj Akavipat (Indiana University, USA), Ana Gabriela Maguitman (Universidad Nacional del Sur, Argentina) and Filippo Menczer (Indiana University, USA)
DOI: 10.4018/978-1-59904-543-6.ch009
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This chapter proposed a collaborative peer network application called 6Search (6S) to address the scalability limitations of centralized search engines. Each peer crawls the Web in a focused way, guided by its user’s information context. Through this approach, better (distributed) coverage can be achieved. Each peer also acts as a search “servent” (server + client) by submitting and responding to queries to/from its neighbors. This search process has no centralized bottleneck. Peers depend on a local adaptive routing algorithm to dynamically change the topology of the peer network and search for the best neighbors to answer their queries. We present and evaluate learning techniques to improve local query routing. We validate prototypes of the 6S network via simulations with model users based on actual Web crawls. We find that the network topology rapidly converges from a random network to a small world network, with clusters emerging from user communities with shared interests. We finally compare the quality of the results with those obtained by centralized search engines such as Google.

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