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116
TopicSensitive PageRank
, 2002
"... In the original PageRank algorithm for improving the ranking of searchquery results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we pr ..."
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Cited by 415 (10 self)
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In the original PageRank algorithm for improving the ranking of searchquery results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate queryspecific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topicsensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topicsensitive PageRank scores using the topic of the context in which the query appeared.
Trust management for the semantic web
 In ISWC
, 2003
"... Abstract. Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give ea ..."
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Cited by 196 (3 self)
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Abstract. Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign topdown or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user provides personal trust values for a small number of other users. We compose these trusts to compute the trust a user should place in any other user in the network. A user is not assigned a single trust rank. Instead, different users may have different trust values for the same user. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise. 1.
Authoritybased keyword search in databases
 TODS
"... The ObjectRank system applies authoritybased ranking to keyword search in databases modeled as labeled graphs. Conceptually, authority originates at the nodes (objects) containing the keywords and flows to objects according to their semantic connections. Each node is ranked according to its authori ..."
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Cited by 166 (12 self)
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The ObjectRank system applies authoritybased ranking to keyword search in databases modeled as labeled graphs. Conceptually, authority originates at the nodes (objects) containing the keywords and flows to objects according to their semantic connections. Each node is ranked according to its authority with respect to the particular
Topicsensitive pagerank: A contextsensitive ranking algorithm for web search
 IEEE Transactions on Knowledge and Data Engineering
, 2003
"... Abstract—The original PageRank algorithm for improving the ranking of searchquery results computes a single vector, using the link structure of the Web, to capture the relative “importance ” of Web pages, independent of any particular search query. To yield more accurate search results, we propose ..."
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Cited by 145 (2 self)
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Abstract—The original PageRank algorithm for improving the ranking of searchquery results computes a single vector, using the link structure of the Web, to capture the relative “importance ” of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. For ordinary keyword search queries, we compute the topicsensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topicsensitive PageRank scores using the topic of the context in which the query appeared. By using linear combinations of these (precomputed) biased PageRank vectors to generate contextspecific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. We describe techniques for efficiently implementing a largescale search system based on the topicsensitive PageRank scheme. Index Terms—Web search, web graph, link analysis, PageRank, search in context, personalized search, ranking algorithm.
Deeper inside pagerank
 Internet Mathematics
, 2004
"... Abstract. This paper serves as a companion or extension to the “Inside PageRank” paper by Bianchini et al. [Bianchini et al. 03]. It is a comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existe ..."
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Cited by 142 (4 self)
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Abstract. This paper serves as a companion or extension to the “Inside PageRank” paper by Bianchini et al. [Bianchini et al. 03]. It is a comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties, possible alterations to the basic model, suggested alternatives to the traditional solution methods, sensitivity and conditioning, and finally the updating problem. We introduce a few new results, provide an extensive reference list, and speculate about exciting areas of future research. 1.
Extrapolation Methods for Accelerating PageRank Computations
 In Proceedings of the Twelfth International World Wide Web Conference
, 2003
"... We present a novel algorithm for the fast computation of PageRank, a hyperlinkbased estimate of the "importance" of Web pages. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web li ..."
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Cited by 134 (13 self)
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We present a novel algorithm for the fast computation of PageRank, a hyperlinkbased estimate of the "importance" of Web pages. The original PageRank algorithm uses the Power Method to compute successive iterates that converge to the principal eigenvector of the Markov matrix representing the Web link graph. The algorithm presented here, called Quadratic Extrapolation, accelerates the convergence of the Power Method by periodically subtracting off estimates of the nonprincipal eigenvectors from the current iterate of the Power Method. In Quadratic Extrapolation, we take advantage of the fact that the first eigenvalueof a Markov matrix is known to be 1 to compute the nonprincipal eigenvectorsusing successiveiterates of the Power Method. Empirically, we show that using Quadratic Extrapolation speeds up PageRank computation by 50300% on a Web graph of 80 million nodes, with minimal overhead.
Exploiting the Block Structure of the Web for Computing PageRank
, 2003
"... The web link graph has a nested block structure: the vast majority of hyperlinks link pages on a host to other pages on the same host, and many of those that do not link pages within the same domain. We show how to exploit this structure to speed up the computation of PageRank by a 3stage alg ..."
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Cited by 129 (5 self)
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The web link graph has a nested block structure: the vast majority of hyperlinks link pages on a host to other pages on the same host, and many of those that do not link pages within the same domain. We show how to exploit this structure to speed up the computation of PageRank by a 3stage algorithm whereby (1) the local PageRanks of pages for each host are computed independently using the link structure of that host, (2) these local PageRanks are then weighted by the "importance" of the corresponding host, and (3) the standard PageRank algorithm is then run using as its starting vector the weighted concatenation of the local PageRanks. Empirically, this algorithm speeds up the computation of PageRank by a factor of 2 in realistic scenarios. Further, we develop a variant of this algorithm that efficiently computes many different "personalized" PageRanks, and a variant that efficiently recomputes PageRank after node updates.
ODISSEA: A PeertoPeer Architecture for Scalable Web Search and Information Retrieval
 In WebDB
, 2003
"... this paper appears in [15], and updated information is available at http://cis.poly.edu/westlab/odissea/ ..."
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Cited by 87 (3 self)
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this paper appears in [15], and updated information is available at http://cis.poly.edu/westlab/odissea/
Towards scaling fully personalized PageRank
 In Proceedings of the 3rd Workshop on Algorithms and Models for the WebGraph (WAW
, 2004
"... Abstract Personalized PageRank expresses backlinkbased page quality around userselected pages in a similar way as PageRank expresses quality over the entire Web. Existing personalized PageRank algorithms can however serve online queries only for a restricted choice of page selection. In this pape ..."
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Cited by 70 (2 self)
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Abstract Personalized PageRank expresses backlinkbased page quality around userselected pages in a similar way as PageRank expresses quality over the entire Web. Existing personalized PageRank algorithms can however serve online queries only for a restricted choice of page selection. In this paper we achieve full personalization by a novel algorithm that computes a compact database of simulated random walks; this database can serve arbitrary personal choices of small subsets of web pages. We prove that for a fixed error probability, the size of our database is linear in the number of web pages. We justify our estimation approach by asymptotic worstcase lower bounds; we show that exact personalized PageRank values can only be obtained from a database of quadratic size. 1
A survey of eigenvector methods of web information retrieval
 SIAM Rev
"... Abstract. Web information retrieval is significantly more challenging than traditional wellcontrolled, small document collection information retrieval. One main difference between traditional information retrieval and Web information retrieval is the Web’s hyperlink structure. This structure has bee ..."
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Cited by 66 (6 self)
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Abstract. Web information retrieval is significantly more challenging than traditional wellcontrolled, small document collection information retrieval. One main difference between traditional information retrieval and Web information retrieval is the Web’s hyperlink structure. This structure has been exploited by several of today’s leading Web search engines, particularly Google and Teoma. In this survey paper, we focus on Web information retrieval methods that use eigenvector computations, presenting the three popular methods of HITS, PageRank, and SALSA.