Literatur zur Informationserschließung

Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.

© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 28. April 2022)

Suchergebnisse

• 1
  Bauckhage, C.: Marginalizing over the PageRank damping factor.
  In: http://arxiv.org/pdf/1409.0104.pdf.
  
  Abstract: In this note, we show how to marginalize over the damping parameter of the PageRank equation so as to obtain a parameter-free version known as TotalRank. Our discussion is meant as a reference and intended to provide a guided tour towards an interesting result that has applications in information retrieval and classification.
  
  Themenfeld: Suchmaschinen ; Retrievalalgorithmen
  Objekt: PageRank ; Google
  
  
• 2
  Bensman, S.J.: Eugene Garfield, Francis Narin, and PageRank : the theoretical bases of the Google search engine.
  In: http://arxiv.org/abs/1312.3872.
  
  Abstract: This paper presents a test of the validity of using Google Scholar to evaluate the publications of researchers by comparing the premises on which its search engine, PageRank, is based, to those of Garfield's theory of citation indexing. It finds that the premises are identical and that PageRank and Garfield's theory of citation indexing validate each other.
  
  Themenfeld: Suchmaschinen ; Citation indexing
  Objekt: PageRank ; Google Scholar
  
  
• 3
  Oreskovic, A.: Google introduces new 'Hummingbird' search algorithm.
  In: http://www.reuters.com/article/net-us-google-search-idUSBRE98P11O20131002.
  
  Abstract: Google Inc has overhauled its search algorithm, the foundation of the Internet's dominant search engine, to better cope with the longer, more complex queries it has been getting from Web users.
  
  Objekt: Google Hummingbird ; PageRank
  
  
• 4
  Miller, C.C.: Google alters search to handle more complex queries.
  In: http://bits.blogs.nytimes.com/2013/09/26/google-changes-search-to-handle-more-complex-queries/?ref=technology&_r=0.
  
  Abstract: Google on Thursday announced one of the biggest changes to its search engine, a rewriting of its algorithm to handle more complex queries that affects 90 percent of all searches. The change, which represents a new approach to search for Google, required the biggest changes to the company's search algorithm since 2000. Now, Google, the world's most popular search engine, will focus more on trying to understand the meanings of and relationships among things, as opposed to its original strategy of matching keywords.
  
  Inhalt: Artikel in der New York Times.
  
  Objekt: Google Hummingbird ; PageRank
  
  
• 5
  Hummingbird Neuer Suchalgorithmus bei Google.
  In: http://www.golem.de/news/hummingbird-neuer-suchalgorithmus-bei-google-1309-101828.html.
  
  Abstract: Google hat mit "Hummingbird" einen neuen Suchalgorithmus entwickelt und bereits eingeführt. Dabei handelt es sich laut Google um eine der größten Veränderungen der Suchmaschine, die rund 90 Prozent aller Suchanfragen betrifft. Im Rahmen einer kleinen Veranstaltung zum 15. Geburtstag der Suchmaschine hat Google in die Garage geladen, in der das Unternehmen gegründet wurde. Dabei enthüllte Google eine der bisher größten Veränderungen an der Suchmaschine: Ohne dass Nutzer etwas davon mitbekamen, hat Google vor rund einem Monat seinen Suchalgorithmus ausgetauscht. Der neue Suchalgorithmus mit Codenamen "Hummingbird" soll es Google ermöglichen, Suchanfragen und Beziehungen zwischen Dingen besser zu verstehen. Das soll die Suchmaschine in die Lage versetzen, komplexere Suchanfragen zu verarbeiten, die von Nutzern immer häufiger gestellt werden - auch, weil immer mehr Nutzer Google auf dem Smartphone per Spracheingabe nutzen. Früher versuchte Google lediglich, die Schlüsselwörter in einer Suchanfrage in Webseiten wiederzufinden. Doch seit geraumer Zeit arbeitet Google daran, die Suchanfragen besser zu verstehen, um bessere Suchergebnisse anzuzeigen.
  
  Objekt: Google Hummingbird ; PageRank
  
  
• 6
  Ding, Y.: Applying weighted PageRank to author citation networks.
  In: Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.236-245.
  
  Abstract: This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures
  
  Themenfeld: Informetrie
  Objekt: PageRank
  
  
• 7
  Ding, Y.: Topic-based PageRank on author cocitation networks.
  In: Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.449-466.
  
  Abstract: Ranking authors is vital for identifying a researcher's impact and standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The author-conference-topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal component analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t.
  
  Themenfeld: Retrievalalgorithmen
  Objekt: PageRank
  
  
• 8
  Yan, E. ; Ding, Y.: Discovering author impact : a PageRank perspective.
  In: Information processing and management. 47(2011) no.1, S.125-134.
  
  Abstract: This article provides an alternative perspective for measuring author impact by applying PageRank algorithm to a coauthorship network. A weighted PageRank algorithm considering citation and coauthorship network topology is proposed. We test this algorithm under different damping factors by evaluating author impact in the informetrics research community. In addition, we also compare this weighted PageRank with the h-index, citation, and program committee (PC) membership of the International Society for Scientometrics and Informetrics (ISSI) conferences. Findings show that this weighted PageRank algorithm provides reliable results in measuring author impact.
  
  Themenfeld: Informetrie
  Objekt: PageRank
  
  
• 9
  Bressan, M. ; Peserico, E.: Choose the damping, choose the ranking?.
  In: Journal of discrete algorithms. 8(2010) no.2, S.199-213.
  
  Abstract: To what extent can changes in PageRank's damping factor affect node ranking? We prove that, at least on some graphs, the top k nodes assume all possible k! orderings as the damping factor varies, even if it varies within an arbitrarily small interval (e.g. [0.84999,0.85001][0.84999,0.85001]). Thus, the rank of a node for a given (finite set of discrete) damping factor(s) provides very little information about the rank of that node as the damping factor varies over a continuous interval. We bypass this problem introducing lineage analysis and proving that there is a simple condition, with a "natural" interpretation independent of PageRank, that allows one to verify "in one shot" if a node outperforms another simultaneously for all damping factors and all damping variables (informally, time variant damping factors). The novel notions of strong rank and weak rank of a node provide a measure of the fuzziness of the rank of that node, of the objective orderability of a graph's nodes, and of the quality of results returned by different ranking algorithms based on the random surfer model. We deploy our analytical tools on a 41M node snapshot of the .it Web domain and on a 0.7M node snapshot of the CiteSeer citation graph. Among other findings, we show that rank is indeed relatively stable in both graphs; that "classic" PageRank (d=0.85) marginally outperforms Weighted In-degree (d->0), mainly due to its ability to ferret out "niche" items; and that, for both the Web and CiteSeer, the ideal damping factor appears to be 0.8-0.9 to obtain those items of high importance to at least one (model of randomly surfing) user, but only 0.5-0.6 to obtain those items important to every (model of randomly surfing) user.
  
  Inhalt: This paper addresses the fundamental question of how the ranking induced by PageRank can be affected by variations of the damping factor. This introduction briefly reviews the PageRank algorithm (Section 1.1) and the crucial difference between score and rank (Section 1.2) before presenting an overview of our results and the organization of the rest of the paper (Section 1.3). Vgl. auch: doi:10.1016/j.jda.2009.11.001. http://www.sciencedirect.com/science/article/pii/S1570866709000926.
  
  Themenfeld: Suchmaschinen
  Objekt: PageRank
  
  
• 10
  Li, J. ; Willett, P.: ArticleRank : a PageRank-based alternative to numbers of citations for analysing citation networks.
  In: Aslib proceedings. 61(2009) no.6, S.605-618.
  
  Abstract: Purpose - The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations. Design/methodology/approach - ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets - a citation network based on an early paper on webometrics, and a self-citation network based on the 19 most cited papers in the Journal of Documentation - using citation data taken from the Web of Knowledge database. Findings - ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset. Originality/value - This is a novel application of the PageRank algorithm.
  
  Themenfeld: Retrievalalgorithmen ; Informetrie
  Objekt: ArticleRank ; PageRank
  
  
• 11
  Dambeck, H.: Wie Google mit Milliarden Unbekannten rechnet : Teil 2: Ausgerechnet: Der Page Rank für ein Mini-Web aus drei Seiten.
  In: http://www.spiegel.de/wissenschaft/mensch/0,1518,646448-2,00.html.
  
  Abstract: Ein simples Beispiel eines Mini-Internets aus drei Web-Seiten verdeutlicht, wie dieses Ranking-System in der Praxis funktioniert.
  
  Inhalt: Text ist ein Auszug aus dem Buch "Numerator - Mathematik für jeden", das im Goldmann-Verlag erschienen ist.
  
  Themenfeld: Suchmaschinen
  Objekt: Google ; PageRank
  
  
• 12
  Dambeck, H.: Wie Google mit Milliarden Unbekannten rechnet : Teil.1.
  In: http://www.spiegel.de/wissenschaft/mensch/0,1518,646448,00.html.
  
  Abstract: Ein Leben ohne Suchmaschinen? Für alle, die viel im World Wide Web unterwegs sind, eine geradezu absurde Vorstellung. Bei der Berechnung der Trefferlisten nutzt Google ein erstaunlich simples mathematisches Verfahren, das sogar Milliarden von Internetseiten in den Griff bekommt.
  
  Inhalt: Text ist ein Auszug aus dem Buch "Numerator - Mathematik für jeden", das im Goldmann-Verlag erschienen ist.
  
  Themenfeld: Suchmaschinen
  Objekt: Google ; PageRank
  
  
• 13
  Ding, Y. ; Yan, E. ; Frazho, A. ; Caverlee, J.: PageRank for ranking authors in co-citation networks.
  In: Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2229-2243.
  
  Abstract: This paper studies how varied damping factors in the PageRank algorithm influence the ranking of authors and proposes weighted PageRank algorithms. We selected the 108 most highly cited authors in the information retrieval (IR) area from the 1970s to 2008 to form the author co-citation network. We calculated the ranks of these 108 authors based on PageRank with the damping factor ranging from 0.05 to 0.95. In order to test the relationship between different measures, we compared PageRank and weighted PageRank results with the citation ranking, h-index, and centrality measures. We found that in our author co-citation network, citation rank is highly correlated with PageRank with different damping factors and also with different weighted PageRank algorithms; citation rank and PageRank are not significantly correlated with centrality measures; and h-index rank does not significantly correlate with centrality measures but does significantly correlate with other measures. The key factors that have impact on the PageRank of authors in the author co-citation network are being co-cited with important authors.
  
  Themenfeld: Suchmaschinen ; Retrievalalgorithmen ; Informetrie
  Objekt: PageRank
  
  
• 14
  Pasquinelli, M.: Googles PageRank : Diagramm des kognitiven Kapitalismus und Rentier des gemeinsamen Wissens.
  In: Deep Search: Politik des Suchens jenseits von Google; Deep Search-Konferenz, Wien, 2008.11.08; eine Veröffentlichung des World-Information Institute. Hrsg.: K. Becker u. F. Stalder. Innsbruck : Studien-Verl., 2009. S.171-181.
  
  Abstract: Ein Großteil der Kritik an Google konzentriert sich auf das imperiale Wesen seines Monopols: seine dominante Position, die Datenschutzprobleme, die Zensur, die globale dataveillance. Studien zur molekularen Ökonomie im Innersten dieser Vorherrschaft gibt es dagegen nur wenige. Während viele kritische Beiträge zu Google Foucaults Jargon missbrauchen und sich der Vorstellung eines digitalen Panoptikons hingeben, entspringt die Macht Googles einer ökonomischen Matrix, die von der kabbalistischen Formel des PageRank bestimmt wird - jenem ausgeklügelten Algorithmus, der die Wichtigkeit einer Webseite und die Hierarchie der Google-Suchresultate bestimmt. Wie sich im folgenden zeigen wird, lässt sich die Funktion von PageRank problemlos nachvollziehen. Eine "politische Ökonomie" dieses Apparats ist jedoch noch ausständig.
  
  Themenfeld: Suchmaschinen
  Wissenschaftsfach: Kommunikationswissenschaften
  Objekt: Google ; PageRank
  
  
• 15
  Cheng, S. ; YunTao, P. ; JunPeng, Y. ; Hong, G. ; ZhengLu, Y. ; ZhiYu, H.: PageRank, HITS and impact factor for journal ranking.
  In: Proceeding CSIE '09: Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 06. Washington, DC : IEEE Computer Society, 2009. S.285-290.
  
  Abstract: Journal citation measures are one of the most widely used bibliometric tools. The most well-known measure is the ISI Impact Factor, under the standard definition, the impact factor of journal j in a given year is the average number of citations received by papers published in the previous two years of journal j. However, the impact factor has its "intrinsic" limitations, it is a ranking measure based fundamentally on a pure counting of the in-degrees of nodes in the network, and its calculation does not take into account the "impact" or "prestige" of the journals in which the citations appear. Google's PageRank algorithm and Kleinberg's HITS method are webpage ranking algorithm, they compute the scores of webpages based on a combination of the number of hyperlinks that point to the page and the status of pages that the hyperlinks originate from, a page is important if it is pointed to by other important pages. We demonstrate how popular webpage algorithm PageRank and HITS can be used ranking journal, and we compared ISI impact factor, PageRank and HITS for journal ranking, and with PageRank and HITS compute respectively including self-citation and non self-citation, and discussed the merit and shortcomings and the scope of application that the various algorithms are used to rank journal.
  
  Inhalt: Vgl.: DOI: 10.1109/CSIE.2009.351.
  
  Themenfeld: Suchmaschinen ; Informetrie
  Objekt: PageRank ; HITS
  
  
• 16
  Ma, N. ; Guan, J. ; Zhao, Y.: Bringing PageRank to the citation analysis.
  In: Information processing and management. 44(2008) no.2, S.800-810.
  
  Abstract: The paper attempts to provide an alternative method for measuring the importance of scientific papers based on the Google's PageRank. The method is a meaningful extension of the common integer counting of citations and is then experimented for bringing PageRank to the citation analysis in a large citation network. It offers a more integrated picture of the publications' influence in a specific field. We firstly calculate the PageRanks of scientific papers. The distributional characteristics and comparison with the traditionally used number of citations are then analyzed in detail. Furthermore, the PageRank is implemented in the evaluation of research influence for several countries in the field of Biochemistry and Molecular Biology during the time period of 2000-2005. Finally, some advantages of bringing PageRank to the citation analysis are concluded.
  
  Themenfeld: Citation indexing
  Objekt: PageRank
  
  
• 17
  Kandelberg, A.: PageRank im Detail.[Präsentation].
  In: http://ls2-www.cs.uni-dortmund.de/~sauerhof/sm0708/vortrag5.pdf.
  
  Themenfeld: Suchmaschinen
  Objekt: PageRank
  
  
• 18
  Wills, R.S.: Google's PageRank : the math behind the search engine.
  In: Mathematical intelligencer. 28(2006) no.4, S.6-11.
  
  Abstract: Approximately 91 million American adults use the Internet on a typical day The number-one Internet activity is reading and writing e-mail. Search engine use is next in line and continues to increase in popularity. In fact, survey findings indicate that nearly 60 million American adults use search engines on a given day. Even though there are many Internet search engines, Google, Yahoo!, and MSN receive over 81% of all search requests. Despite claims that the quality of search provided by Yahoo! and MSN now equals that of Google, Google continues to thrive as the search engine of choice, receiving over 46% of all search requests, nearly double the volume of Yahoo! and over four times that of MSN. I use Google's search engine on a daily basis and rarely request information from other search engines. One day, I decided to visit the homepages of Google. Yahoo!, and MSN to compare the quality of search results. Coffee was on my mind that day, so I entered the simple query "coffee" in the search box at each homepage. Table 1 shows the top ten (unsponsored) results returned by each search engine. Although ordered differently, two webpages, www.peets.com and www.coffeegeek.com, appear in all three top ten lists. In addition, each pairing of top ten lists has two additional results in common. Depending on the information I hoped to obtain about coffee by using the search engines, I could argue that any one of the three returned better results: however, I was not looking for a particular webpage, so all three listings of search results seemed of equal quality. Thus, I plan to continue using Google. My decision is indicative of the problem Yahoo!, MSN, and other search engine companies face in the quest to obtain a larger percentage of Internet search volume. Search engine users are loyal to one or a few search engines and are generally happy with search results. Thus, as long as Google continues to provide results deemed high in quality, Google likely will remain the top search engine. But what set Google apart from its competitors in the first place? The answer is PageRank. In this article I explain this simple mathematical algorithm that revolutionized Web search.
  
  Themenfeld: Suchmaschinen ; Retrievalalgorithmen
  Objekt: PageRank
  
  
• 19
  Langville, A.N. ; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings.
  Princeton : Princeton Univ. Press, 2006. X, 224 S.
  ISBN 0-691-12202-4
  
  Abstract: Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other Web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of Web page rankings, "Google's PageRank and Beyond" supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample Web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. It includes: many illustrative examples and entertaining asides; MATLAB code; accessible and informal style; and complete and self-contained section for mathematics review.
  
  Inhalt: Inhalt: Chapter 1. Introduction to Web Search Engines: 1.1 A Short History of Information Retrieval - 1.2 An Overview of Traditional Information Retrieval - 1.3 Web Information Retrieval Chapter 2. Crawling, Indexing, and Query Processing: 2.1 Crawling - 2.2 The Content Index - 2.3 Query Processing Chapter 3. Ranking Webpages by Popularity: 3.1 The Scene in 1998 - 3.2 Two Theses - 3.3 Query-Independence Chapter 4. The Mathematics of Google's PageRank: 4.1 The Original Summation Formula for PageRank - 4.2 Matrix Representation of the Summation Equations - 4.3 Problems with the Iterative Process - 4.4 A Little Markov Chain Theory - 4.5 Early Adjustments to the Basic Model - 4.6 Computation of the PageRank Vector - 4.7 Theorem and Proof for Spectrum of the Google Matrix Chapter 5. Parameters in the PageRank Model: 5.1 The a Factor - 5.2 The Hyperlink Matrix H - 5.3 The Teleportation Matrix E Chapter 6. The Sensitivity of PageRank; 6.1 Sensitivity with respect to alpha - 6.2 Sensitivity with respect to H - 6.3 Sensitivity with respect to vT - 6.4 Other Analyses of Sensitivity - 6.5 Sensitivity Theorems and Proofs Chapter 7. The PageRank Problem as a Linear System: 7.1 Properties of (I - alphaS) - 7.2 Properties of (I - alphaH) - 7.3 Proof of the PageRank Sparse Linear System Chapter 8. Issues in Large-Scale Implementation of PageRank: 8.1 Storage Issues - 8.2 Convergence Criterion - 8.3 Accuracy - 8.4 Dangling Nodes - 8.5 Back Button Modeling ; Chapter 9. Accelerating the Computation of PageRank: 9.1 An Adaptive Power Method - 9.2 Extrapolation - 9.3 Aggregation - 9.4 Other Numerical Methods Chapter 10. Updating the PageRank Vector: 10.1 The Two Updating Problems and their History - 10.2 Restarting the Power Method - 10.3 Approximate Updating Using Approximate Aggregation - 10.4 Exact Aggregation - 10.5 Exact vs. Approximate Aggregation - 10.6 Updating with Iterative Aggregation - 10.7 Determining the Partition - 10.8 Conclusions Chapter 11. The HITS Method for Ranking Webpages: 11.1 The HITS Algorithm - 11.2 HITS Implementation - 11.3 HITS Convergence - 11.4 HITS Example - 11.5 Strengths and Weaknesses of HITS - 11.6 HITS's Relationship to Bibliometrics - 11.7 Query-Independent HITS - 11.8 Accelerating HITS - 11.9 HITS Sensitivity Chapter 12. Other Link Methods for Ranking Webpages: 12.1 SALSA - 12.2 Hybrid Ranking Methods - 12.3 Rankings based on Traffic Flow Chapter 13. The Future of Web Information Retrieval: 13.1 Spam - 13.2 Personalization - 13.3 Clustering - 13.4 Intelligent Agents - 13.5 Trends and Time-Sensitive Search - 13.6 Privacy and Censorship - 13.7 Library Classification Schemes - 13.8 Data Fusion Chapter 14. Resources for Web Information Retrieval: 14.1 Resources for Getting Started - 14.2 Resources for Serious Study Chapter 15. The Mathematics Guide: 15.1 Linear Algebra - 15.2 Perron-Frobenius Theory - 15.3 Markov Chains - 15.4 Perron Complementation - 15.5 Stochastic Complementation - 15.6 Censoring - 15.7 Aggregation - 15.8 Disaggregation
  
  Themenfeld: Suchmaschinen ; Retrievalalgorithmen
  Objekt: Google ; PageRank ; HITS-Algorithmus
  
  RSWK: Google / Web-Seite / Rangstatistik (HEBIS) ; Webpage / Rangstatistik (GBV) ; Google / Suchmaschine / Ranking (BVB)
  BK: 54.65 / Webentwicklung / Webanwendungen ; 31.80 / Angewandte Mathematik ; 54.32 / Rechnerkommunikation ; 06.74 / Informationssysteme ; 06.70 / Katalogisierung / Bestandserschließung
  
• 20
  Baeza-Yates, R. ; Boldi, P. ; Castillo, C.: Generalizing PageRank : damping functions for linkbased ranking algorithms.
  In: http://chato.cl/papers/baeza06_general_pagerank_damping_functions_link_ranking.pdf [Proceedings of the ACM Special Interest Group on Information Retrieval (SIGIR) Conference, SIGIR'06, August 6-10, 2006, Seattle, Washington, USA].
  
  Abstract: This paper introduces a family of link-based ranking algorithms that propagate page importance through links. In these algorithms there is a damping function that decreases with distance, so a direct link implies more endorsement than a link through a long path. PageRank is the most widely known ranking function of this family. The main objective of this paper is to determine whether this family of ranking techniques has some interest per se, and how different choices for the damping function impact on rank quality and on convergence speed. Even though our results suggest that PageRank can be approximated with other simpler forms of rankings that may be computed more efficiently, our focus is of more speculative nature, in that it aims at separating the kernel of PageRank, that is, link-based importance propagation, from the way propagation decays over paths. We focus on three damping functions, having linear, exponential, and hyperbolic decay on the lengths of the paths. The exponential decay corresponds to PageRank, and the other functions are new. Our presentation includes algorithms, analysis, comparisons and experiments that study their behavior under different parameters in real Web graph data. Among other results, we show how to calculate a linear approximation that induces a page ordering that is almost identical to PageRank's using a fixed small number of iterations; comparisons were performed using Kendall's tau on large domain datasets.
  
  Themenfeld: Suchmaschinen
  Objekt: PageRank