Mining Human Mobility in Location-Based Social Networks PDF
Preview Mining Human Mobility in Location-Based Social Networks
Mining Human Mobility in Location-Based Social Networks Synthesis Lectures on Data Mining and Knowledge Discovery Editors JiaweiHan,UniversityofIllinoisatUrbana-Champaign LiseGetoor,UniversityofMaryland WeiWang,UniversityofNorthCarolina,ChapelHill JohannesGehrke,CornellUniversity RobertGrossman,UniversityofChicago SynthesisLecturesonDataMiningandKnowledgeDiscoveryiseditedbyJiaweiHan,Lise Getoor,WeiWang,JohannesGehrke,andRobertGrossman.eseriespublishes50-to150-page publicationsontopicspertainingtodatamining,webmining,textmining,andknowledgediscovery, includingtutorialsandcasestudies.Potentialtopicsinclude:dataminingalgorithms,innovativedata miningapplications,dataminingsystems,miningtext,webandsemi-structureddata,high performanceandparallel/distributeddatamining,dataminingstandards,dataminingand knowledgediscoveryframeworkandprocess,dataminingfoundations,miningdatastreamsand sensordata,miningmulti-mediadata,miningsocialnetworksandgraphdata,miningspatialand temporaldata,pre-processingandpost-processingindatamining,robustandscalablestatistical methods,security,privacy,andadversarialdatamining,visualdatamining,visualanalytics,anddata visualization. MiningHumanMobilityinLocation-BasedSocialNetworks HuijiGaoandHuanLiu 2015 MiningLatentEntityStructures ChiWangandJiaweiHan 2015 ProbabilisticApproachestoRecommendations NicolaBarbieri,GiuseppeManco,andEttoreRitacco 2014 OutlierDetectionforTemporalData ManishGupta,JingGao,CharuAggarwal,andJiaweiHan 2014 iii ProvenanceDatainSocialMedia GeoffreyBarbier,ZhuoFeng,PritamGundecha,andHuanLiu 2013 GraphMining:Laws,Tools,andCaseStudies D.ChakrabartiandC.Faloutsos 2012 MiningHeterogeneousInformationNetworks:PrinciplesandMethodologies YizhouSunandJiaweiHan 2012 PrivacyinSocialNetworks ElenaZheleva,EvimariaTerzi,andLiseGetoor 2012 CommunityDetectionandMininginSocialMedia LeiTangandHuanLiu 2010 EnsembleMethodsinDataMining:ImprovingAccuracyroughCombiningPredictions GiovanniSeniandJohnF.Elder 2010 ModelingandDataMininginBlogosphere NitinAgarwalandHuanLiu 2009 Copyright©2015byMorgan&Claypool Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotations inprintedreviews,withoutthepriorpermissionofthepublisher. MiningHumanMobilityinLocation-BasedSocialNetworks HuijiGaoandHuanLiu www.morganclaypool.com ISBN:9781627054126 paperback ISBN:9781627054133 ebook DOI10.2200/S00630ED1V01Y201502DMK011 APublicationintheMorgan&ClaypoolPublishersseries SYNTHESISLECTURESONDATAMININGANDKNOWLEDGEDISCOVERY Lecture#11 SeriesEditors:JiaweiHan,UniversityofIllinoisatUrbana-Champaign LiseGetoor,UniversityofMaryland WeiWang,UniversityofNorthCarolina,ChapelHill JohannesGehrke,CornellUniversity RobertGrossman,UniversityofChicago SeriesISSN Print2151-0067 Electronic2151-0075 Mining Human Mobility in Location-Based Social Networks Huiji Gao LinkedIn Huan Liu ArizonaStateUniversity SYNTHESISLECTURESONDATAMININGANDKNOWLEDGE DISCOVERY#11 M &C Morgan &cLaypool publishers ABSTRACT Inrecentyears,therehasbeenarapidgrowthoflocation-basedsocialnetworkingservices,suchas FoursquareandFacebookPlaces,whichhaveattractedanincreasingnumberofusersandgreatly enriched their urban experience. Typical location-based social networking sites allow a user to “checkin”atareal-worldPOI(pointofinterest,e.g.,ahotel,restaurant,theater,etc.),leavetips toward the POI, and share the check-in with their online friends. e check-in action bridges thegapbetweenrealworldandonlinesocialnetworks,resultinginanewtypeofsocialnetworks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location- basedsocialnetworksdatacontainsuniquepropertieswithabundantheterogeneousinformation torevealhumanmobility,i.e.,“whenandwhereauser(who)hasbeentoforwhat,”correspond- ingtoanunprecedentedopportunitytobetterunderstandhumanmobilityfromspatial,tempo- ral, social, and content aspects. e mining and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing torecommendersystems,providingusersmoreconvenientlifeexperiencethanbefore.isbook takes a data mining perspective to offer an overview of studying human mobility in location- based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illus- trative examples and real-world LBSN datasets, and discusses effective evaluation methods in mininghumanmobility.Inparticular,weillustrateuniquecharacteristicsandresearchopportu- nities of LBSN data, present representative tasks of mining human mobility on location-based socialnetworks,includingcapturingusermobilitypatternstounderstandwhenandwhereauser commonlygoes(locationprediction),andexploitinguserpreferencesandlocationprofilestoin- vestigatewhereandwhenauserwantstoexplore(locationrecommendation),alongwithstudying auser’scheck-inactivityintermsofwhyausergoestoacertainlocation. KEYWORDS location-basedsocialnetworks,humanmobility,socialmedia,datamining,location recommendation,locationprediction vii is effort is dedicated to my parents and Ye. ank you for everything. -HG To my parents, wife, and sons. -HL
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