Efficient and decentralized PageRank approximation in a peer-to-peer web search network
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Size doesn't always matter: exploiting pageRank for query routing in distributed IR
P2PIR '06 Proceedings of the international workshop on Information retrieval in peer-to-peer networks
P2P authority analysis for social communities
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficiently Handling Dynamics in Distributed Link Based Authority Analysis
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Mining the “Voice of the Customer” for Business Prioritization
ACM Transactions on Intelligent Systems and Technology (TIST)
Towards a common framework for peer-to-peer web retrieval
From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments
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The link structure of the Web graph is used in algorithms such as Kleinbergýs HITS and Googleýs PageRank to assign authoritative weights to Web pages and thus rank them. Both require a centralized computation of the ranking if used to rank the complete Web graph. In this paper, we propose a new approach based on a Layered Markov Model to distinguish transitions among Web sites and Web documents. Based on this model, we propose two different approaches for computation of ranking of Web documents, a centralized one and a decentralized one. Both produce a well-defined ranking for a given Web graph. We then formally prove that the two approaches are equivalent. This provides a theoretical foundation for decomposing link-based rank computation and makes the computation for a Web-scale graph feasible in a decentralized fashion, such as required for Web search engines having a peer-to-peer architecture. Furthermore, personalized rankings can be produced by adapting the computation at both the local layer and the global layer. Our empirical results show that the ranking generated by our model is qualitatively comparable to or even better than the ranking produced by PageRank.