1 / 27

lecture pagerank

lecture pagerank

Download Presentation

lecture pagerank

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. ThePageRankCitationRanking: BringOrdertotheweb LawrencePage,SergeyBrin,RajeevMotwaniandTerryWinograd n PresentedbyFeiLi n 1

  2. MotivationandIntroduction WhyisPageImportanceRatingimportant? –NewchallengesforinformationretrievalontheWorld WideWeb. •Hugenumberofwebpages:150millionby1998 1000billionby2008 •Diversityofwebpages:differenttopics,differentquality,etc. WhatisPageRank? n n Amethodforratingtheimportanceofwebpages objectivelyandmechanicallyusingthelinkstructureof theweb. •

  3. TheHistoryofPageRank PageRankwasdevelopedbyLarryPage(hence thenamePage-Rank)andSergeyBrin. Itisfirstaspartofaresearchprojectaboutanew kindofsearchengine.Thatprojectstartedin1995 andledtoafunctionalprototypein1998. Shortlyafter,PageandBrinfoundedGoogle. n n n n16billion…

  4. RecentNews TherearesomenewsaboutthatPageRankwillbe canceledbyGoogle. TherearelargenumbersofSearchEngine Optimization(SEO). SEOusedifferenttrickmethodstomakeaweb pagemoreimportantundertheratingofPageRank. n n n

  5. LinkStructureoftheWeb 150millionwebpagesà1.7billionlinks BacklinksandForwardlinks: ØAandBareC’sbacklinks ØCisAandB’sforwardlink n Intuitively,awebpageisimportantifithasalotofbacklinks. Whatifawebpagehasonlyonelinkoffwww.yahoo.com?

  6. ASimpleVersionofPageRank u:awebpage n nBu:thesetofu’sbacklinks nNv:thenumberofforwardlinksof pagev nc:thenormalizationfactortomake ||R||L1=1(||R||L1=|R1+…+Rn|)

  7. AnexampleofSimplifiedPageRank PageRankCalculation:firstiteration

  8. AnexampleofSimplifiedPageRank PageRankCalculation:seconditeration

  9. AnexampleofSimplifiedPageRank Convergenceaftersomeiterations

  10. AProblemwithSimplifiedPageRank Aloop: Duringeachiteration,theloopaccumulates rankbutneverdistributesranktootherpages!

  11. AnexampleoftheProblem

  12. AnexampleoftheProblem

  13. AnexampleoftheProblem

  14. RandomWalksinGraphs TheRandomSurferModel –Thesimplifiedmodel:thestandingprobability distributionofarandomwalkonthegraphof theweb.simplykeepsclickingsuccessive linksatrandom TheModifiedModel –Themodifiedmodel:the“randomsurfer” simplykeepsclickingsuccessivelinksat random,butperiodically“getsbored”and jumpstoarandompagebasedonthe distributionofE n n

  15. ModifiedVersionofPageRank E(u):adistributionofranksofwebpagesthat“users”jumpto whenthey“getsbored”aftersuccessivelinksatrandom.

  16. AnexampleofModifiedPageRank 16

  17. DanglingLinks Linksthatpointtoanypagewithnooutgoing links Mostarepagesthathavenotbeen downloadedyet Affectthemodelsinceitisnotclearwhere theirweightshouldbedistributed Donotaffecttherankingofanyotherpage directly Canbesimplyremovedbeforepagerank calculationandaddedbackafterwards n n n n n

  18. PageRankImplementation ConverteachURLintoauniqueintegerandstore eachhyperlinkinadatabaseusingtheintegerIDs toidentifypages SortthelinkstructurebyID Removeallthedanglinglinksfromthedatabase Makeaninitialassignmentofranksandstart iteration n n n n Choosingagoodinitialassignmentcanspeedupthepagerank n Addingthedanglinglinksback. n

  19. ConvergenceProperty PR(322MillionLinks):52iterations n nPR(161MillionLinks):45iterations nScalingfactorisroughlylinearinlogn

  20. ConvergenceProperty TheWebisanexpander-likegraph –Theoryofrandomwalk:arandomwalkonagraphissaidtobe rapidly-mixingifitquicklyconvergestoalimitingdistribution onthesetofnodesinthegraph.Arandomwalkisrapidly- mixingonagraphifandonlyifthegraphisanexpandergraph. –Expandergraph:everysubsetofnodesShasaneighborhood (setofverticesaccessibleviaoutedgesemanatingfromnodesin S)thatislargerthansomefactorαtimesof|S|.Agraphhasa goodexpansionfactorifandonlyifthelargesteigenvalueis sufficientlylargerthanthesecond-largesteigenvalue. n

  21. SearchingwithPageRank Twosearchengines: –Title-basedsearchengine –Fulltextsearchengine Title-basedsearchengine –Searchesonlythe“Titles” –Findsallthewebpageswhosetitlescontainallthequery words –SortstheresultsbyPageRank –Verysimpleandcheaptoimplement –Titlematchensureshighprecision,andPageRankensures highquality Fulltextsearchengine –CalledGoogle –Examinesallthewordsineverystoreddocumentandalso performsPageRank(RankMerging) –Moreprecisebutmorecomplicated • • • 21

  22. SearchingwithPageRank

  23. SearchingwithPageRank

  24. PersonalizedPageRank ImportantcomponentofPageRankcalculationisE –Avectoroverthewebpages(usedassourceofrank) –Powerfulparametertoadjustthepageranks Evectorcorrespondstothedistributionofwebpagesthat arandomsurferperiodicallyjumpsto InsteadinPersonalizedPageRankEconsistsofasingle webpage n n n

  25. PageRankvs.WebTraffic Somehighlyaccessedwebpageshavelow pagerankpossiblybecause –Peopledonotwanttolinktothesepagesfromtheir ownwebpages(theexampleintheirpaperis pornographicsites…) –Someimportantbacklinksareomitted n useusagedataasastartvectorforPageRank.

  26. ThePageRankProxy

  27. Conclusion isaglobalrankingofallwebpagesbasedon theirlocationsinthewebgraphstructure PageRankusesinformationwhichisexternaltothe webpages–backlinks Backlinksfromimportantpagesaremoresignificant thanbacklinksfromaveragepages Thestructureofthewebgraphisveryusefulfor informationretrievaltasks. n n n n

More Related