Mining Human Mobility in Location-Based Social Networks

117 Pages·2015·2.612 MB·English

by  Huiji Gao, Haun Liu

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:ImprovingAccuracyroughCombiningPredictions
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