Table Of ContentSeries ISSN: 2151-0067 C
Series ISSSerNie: s2 I1S5S1N-0: 0261751-0067 C H C M
H A H MM
SYSNYSTNYHTNHETSHEISESI SSLI SLE LCETCEUTCRUTERUSER SOE SNO NON AKRA KRAB AKRA &&&CCCMMoMororgrgagnana & n & C & Cl aClyalpyaopyopoool olP luP ubPulbilbsilhsihsehrersesrs
DATA MINING AND KNOWLEDGE DISCOVERY B A B
DADTAA MTA IMNININGI NAGN DA NKDN KOWNOLEWDLGEED GDEI SDCIOSVCEORVYERY AR RT AR
T I • T
Series Editors: Jiawei Han, University of Illinois at Urbana-Champaign, I • FA FAL I • FA
SeriesS Eerdiietso Ersd: iJtioarwse: iJ Hiawanei, UHnainv,e Ursnitiyv oefr sIiltlyin oofi Is lalitn Uoirsb aatn Ua-rCbahnaam-pCahiagmn,paign, L O L
Lise Getoor, University of Maryland, Wei Wang, University of North Carolina, Chapel Hill, O U O
Lise GLeitsoeo Gr, eUtonoivr,e Ursnitiyv oefr sMitya royf lManadr, yWlaneid W, Waneig W, Uannigv,e Ursnitiyv oefr sNitoyr otfh NCoarrtohl iCnaa,r oClihnaap,e Cl Hhaiplle,l Hill, U T U Graph Mining
Johannes Gehrke, Cornell University, Robert Grossman, University of Illinois at Chicago T SO T GGrarpaphh M Minininingg
JohanJnoehs aGnnehesr kGe,e Chrokrne,e lCl oUrnneivlle Ursintiyv, eRrsoibtye, rRt oGbreorsts Gmraons, sUmnainv,e Ursnitiyv oefr sIiltlyin oofi Is lalitn Coihs iacat gCohicago S S
O S O
S S
Graph Mining
GraGprha pMhi Mnininging Laws, Tools, and Case Studies
Laws, Tools, and Case Studies LaLwasw, Ts,o Toloso, lasn, adn Cda Csea Sset uSdtuiedsies
LawLs, aTwoos,l sT, oaonlds, Canadse C Satused Sietsudies
Deepayan Chakrabarti, Facebook Inc.
DeepaDyeaenp Cayhaank rCahbaakrtria, bFaarcteib,o Foak cIenboc.ok Inc.
Christos Faloutsos, Carnegie Mellon University
ChrisCtohs rFisatloosu Ftsaolos,u Ctsaorsn,e Cgiae rMneeglileo nM Uelnloivne Ursintiyversity
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What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which G R G
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are the most central nodes in a network? These are the questions that motivate this work. Networks A P A
are thea mreo tsth ec emntorsat lc nenotdreasl inno ad ens eitnw ao rnke?t wTohreks?e Tarhee sthe ea rqeu tehstei oqnuse stthioatn sm tohtaivt amteo tthiviast we othrkis. Nwoetrwk.o Nrkestworks P H P
and graphs appear in many diverse settings, for example in social networks, computer-communication H M H
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and others. G G
and otahnedrs .others. Deepayan Chakrabarti
In this work, first we list several surprising patterns that real graphs tend to follow. Then we give DeDepeaeypaany aCnh Cakhraakbraarbtai rti
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a detailed list of generators that try to mirror these patterns. Generators are important, because they
Christos Faloutsos
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can help with “what if ” scenarios, extrapolations, and anonymization. Then we provide a list of ChCrihstroisst Fosa lFoaultosuotssos
can heclapn w hitehlp “ wwhitaht “iwf ”h sacte inf a”r sioces,n eaxritorasp, oexlattriaopnosl,a atinodn sa, naonndy manioznatyimoniz. aTthioenn. Tweh epnr owveid per ao vliidste oaf list of
powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition
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(SVD)), tensors, and case studies like the famous “pageRank” algorithm and the “HITS” algorithm
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for ranking web search results. Finally, we conclude with a survey of tools and observations from
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related fields like sociology, which provide complementary viewpoints.
relatedr efilealtdesd lfiikeeld sso lciikoel osgoyc,i owlhoigcyh, wprhoicvhid pe rcoovmidpel ecmomenptlaermy evnietawrpy oviinetws.points.
About SYNTHESIs
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Graph Mining
Laws,Tools,andCaseStudies
Synthesis Lectures on Data
Mining and Knowledge
Discovery
Editors
JiaweiHan,UIUC
LiseGetoor,UniversityofMaryland
WeiWang,UniversityofNorthCarolina,ChapelHill
JohannesGehrke,CornellUniversity
RobertGrossman,UniversityofChicago
SynthesisLecturesonDataMiningandKnowledgeDiscoveryiseditedbyJiaweiHan,Lise
Getoor,WeiWang,JohannesGehrke,andRobertGrossman.Theseriespublishes50-to150-page
publicationsontopicspertainingtodatamining,webmining,textmining,andknowledge
discovery,includingtutorialsandcasestudies.Thescopewilllargelyfollowthepurviewofpremier
computerscienceconferences,suchasKDD.Potentialtopicsinclude,butnotlimitedto,data
miningalgorithms,innovativedataminingapplications,dataminingsystems,miningtext,web
andsemi-structureddata,highperformanceandparallel/distributeddatamining,datamining
standards,dataminingandknowledgediscoveryframeworkandprocess,dataminingfoundations,
miningdatastreamsandsensordata,miningmulti-mediadata,miningsocialnetworksandgraph
data,miningspatialandtemporaldata,pre-processingandpost-processingindatamining,robust
andscalablestatisticalmethods,security,privacy,andadversarialdatamining,visualdatamining,
visualanalytics,anddatavisualization.
GraphMining:Laws,Tools,andCaseStudies
D.ChakrabartiandC.Faloutsos
2012
MiningHeterogeneousInformationNetworks:PrinciplesandMethodologies
YizhouSunandJiaweiHan
2012
PrivacyinSocialNetworks
ElenaZheleva,EvimariaTerzi,andLiseGetoor
2012
iii
CommunityDetectionandMininginSocialMedia
LeiTangandHuanLiu
2010
EnsembleMethodsinDataMining:ImprovingAccuracyThroughCombiningPredictions
GiovanniSeniandJohnF.Elder
2010
ModelingandDataMininginBlogosphere
NitinAgarwalandHuanLiu
2009
Copyright© 2012byMorgan&Claypool
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printedreviews,withoutthepriorpermissionofthepublisher.
GraphMining:Laws,Tools,andCaseStudies
D.ChakrabartiandC.Faloutsos
www.morganclaypool.com
ISBN:9781608451159 paperback
ISBN:9781608451166 ebook
DOI10.2200/S00449ED1V01Y201209DMK006
APublicationintheMorgan&ClaypoolPublishersseries
SYNTHESISLECTURESONDATAMININGANDKNOWLEDGEDISCOVERY
Lecture#6
SeriesEditors:JiaweiHan,UIUC
LiseGetoor,UniversityofMaryland
WeiWang,UniversityofNorthCarolina,ChapelHill
JohannesGehrke,CornellUniversity
RobertGrossman,UniversityofChicago
SeriesISSN
SynthesisLecturesonDataMiningandKnowledgeDiscovery
Print2151-0067 Electronic2151-0075
Graph Mining
Laws,Tools,andCaseStudies
D.Chakrabarti
Facebook
C.Faloutsos
CMU
SYNTHESISLECTURESONDATAMININGANDKNOWLEDGEDISCOVERY
#6
M
&C Morgan &cLaypool publishers
ABSTRACT
WhatdoestheWeblooklike? Howcanwefindpatterns,communities,outliers,inasocialnetwork?Which
arethemostcentralnodesinanetwork?Thesearethequestionsthatmotivatethiswork.Networksand
graphs appear in many diverse settings,for example in social networks,computer-communication
networks(intrusiondetection,trafficmanagement),protein-proteininteractionnetworksinbiology,
document-textbipartitegraphsintextretrieval,person-accountgraphsinfinancialfrauddetection,
andothers.
Inthiswork,firstwelistseveralsurprisingpatternsthatrealgraphstendtofollow.Thenwe
giveadetailedlistofgeneratorsthattrytomirrorthesepatterns.Generatorsareimportant,because
theycanhelpwith“whatif”scenarios,extrapolations,andanonymization.Thenweprovidealistof
powerfultoolsforgraphanalysis,andspecificallyspectralmethods(SingularValueDecomposition
(SVD)),tensors,andcasestudieslikethefamous“pageRank”algorithmandthe“HITS”algorithm
for ranking web search results.Finally,we conclude with a survey of tools and observations from
relatedfieldslikesociology,whichprovidecomplementaryviewpoints.
KEYWORDS
data mining,social networks,power laws,graph generators,pagerank,singular value
decomposition.
vii
Christos Faloutsos: To Christina, for her patience, support, and down-to-
earth questions; to Michalis and Petros,for the ’99 paper that started it all.
DeepayanChakrabarti:ToPurnaandmyparents,fortheirsupportandhelp,
and for always being there when I needed them.
