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KDD 2018 TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph Mining (40 min) 148 Background (15 min) Graph models, subgraph isomorphism, subgraph mining, graph clustering Challenges and discussion Machine Learning and Visualization (40 min)

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Page 1: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL

Where we are

D. MOTTIN, E. MÜLLER

ExploratoryGraphAnalysis(40min)

RefinementofQueryResults(40min)

FocusedGraphMining(40min)

148

Background(15min)Graphmodels,subgraphisomorphism,subgraphmining,graphclustering

Challengesanddiscussion

MachineLearningandVisualization(40min)

Davide Mottin
Page 2: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL

Summary of Exploratory Graph Analysis

D. MOTTIN, E. MÜLLER 149

ApproximateQueries• UserqueryisimpreciseBy-Examplemethods• Userqueryisanexampleresult

• Onlyneedapartialknowledgeonthedata

• Noneedforcomplicatequerylanguages(useexamples,partialdescriptions)

• Thequeryadaptstouserneed• Enableexploratorysearchby

usingsmallqueriesonthedata

Query(agraph)

Graph

?

Query(anexample)

Graph

Page 3: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL

Challenges for Exploratory Graph Analysis

D. MOTTIN, E. MÜLLER 150

• Unsupported inmostofthecurrentgraphdatabases• No”universal”indextoanswermultipletypeofqueries• Partitioning methodsforapproximatequeryanswering

Database

• Userinteractivityintheexplorationprocess• Nosolutionsforprobabilisticgraphs• Respondtoqueriesindynamic graphs• Findexamplesinstreaming settings

InformationretrievalInformation

retrieval

• Exploitingquerylogsforpersonalizedqueryanswering• Retrieveresultsinformofdocumentsconvertingthequery

structures

Datamining

Page 4: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL

Summary of Focused Graph Mining

D. MOTTIN, E. MÜLLER 151

Thefocusonindividualuserinterest…asQuery totheGraphMiningSystem…asSeedNode(s) forLocalSearch…asAttributes andWeights

• getorinferuserinterestà unexpectedresults

• interactiveexplorationà intuitiveparametrization

• adaptivegraphminingà individuallocalsearch

Page 5: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL

Challenges for Focused Graph Mining

D. MOTTIN, E. MÜLLER 152

Datamining

scale

Userinteractivityinthegraphminingprocess• unsupportedinmostofthecurrentgraphminingalgorithms• hugevarietyofuserinteractions possible• feedbackloopneedstobeunified andbecomeexchangeable

Revolutionofformalmodelsandsearchalgorithms• insufficientextensionsofexistingmodelsandalgorithms• adaptivesteering ofalgorithmsvs.fixedparametrization• evaluationofalgorithmswithuserstudies

Scalabilityofalgorithmsforreal-timeinteraction• NP-hardproblems,heuristicalgorithms,…,stillnotscalable• exploittheuserinterest forpruningthesearchspace

Page 6: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL

Summary of Refinement of Query Results

D. MOTTIN, E. MÜLLER 153

Refinement• Theuserqueryistoorestrictiveor

toogenericTop-kResults• Queriestypicallyhaveinexact

matchesSkylineQueries• Findsmallsetofinterestingitems

withmanydimensionsandincrementalupdates

• Theusermighthaveaverygenericideaofhowtodescribethestructureofinterest

• Thesystemguidestheusertowardstheanswerwithsimplesteps

• Theresultsareexplainedwithreformulations

• Thequeriescanbeinexact

Q03

Q02Q0

1

Page 7: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL

Challenges for Refinement of Query Results

D. MOTTIN, E. MÜLLER 154

• Profiling ofqueriesforoptimizedperformance• Provenance andexplainability ofqueries• Managinguncertainty indata

• Personalized reformulationsandinteractivity• Facet searchdiscoveryingraphs• Learningofuserpreferenceswhilerefining

• Realtime performancenotachieved• Avoidingtraversetheentirespaceusingqueryworkloadsand

querylogs

Database

Datamining

scale

Page 8: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL D. MOTTIN, E. MÜLLER 155

Towards a graph exploration system

Users

Graph

Page 9: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL D. MOTTIN, E. MÜLLER 156

AdaptiveMethodsandStructures

Intuitivequeries

Interactivealgorithms

Users

Graph

Towards a graph exploration system

Page 10: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL D. MOTTIN, E. MÜLLER 157

Interactivity

Personalization

Adaptivity

Scalability

Missing tiles for Graph Exploration

Page 11: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

Not all who wander are lost.J R R Tolkien

D. MOTTIN, E. MÜLLER 158

Slides:http://bit.ly/graph-exp-kdd

Moreongraphs?Visitournew papers:• AntonTsistulin,DavideMottin,PanagiotisKarras,AlexBronstein, EmmanuelMüller. NetLSD:Hearingthe

ShapeofaGraph. KDD2018– Tuesday16.00-18.00- ICCCapitalSuiteRoom6+13• AntonTsistulin,DavideMottin,PanagiotisKarras,AlexBronstein, EmmanuelMüller. SGR:Self-

SupervisedSpectralGraphRepresentationLearning. DLDay - Postersession• LukasFaber,TaraSafavi, DavideMottin,Danai Koutra,EmmanuelMüller. AdaptivePersonalized

KnowledgeGraphSummarization.MLGWorkshop@ KDD

Graph e lorationLetmeShowwhatis RelevantinyourGraph

Page 12: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL

Acknowledgements

Wethanktheauthorsofthepaperswhoprovidedsomeoftheslidesincludedinthistutorial.

Page 13: Where we are - Hasso Plattner Institute · KDD 2018TUTORIAL Where we are D. MOTTIN, E. MÜLLER Exploratory Graph Analysis (40 min) Refinement of Query Results (40 min) Focused Graph

KDD 2018 TUTORIAL

References

[Ma14]Ma,S.,Cao,Y.,Fan,W.,Huai,J.andWo,T.Strongsimulation:Capturingtopologyingraphpatternmatching. TODS,2014[Fan10]Fan,W.,Li,J.,Ma,S.,Wang,H.andWu,Y..Graphhomomorphismrevisitedforgraphmatching. PVLDB,2010[Khan13]Khan,A.,Wu,Y.,Aggarwal,C.C.andYan,X.Nema:Fastgraphsearchwithlabelsimilarity.PVLDB,2013[Yang14]Yang,S.,Wu,Y.,Sun,H.andYan,X.Schemaless andstructurelessgraphquerying.PVLDB,2014.[Mottin14]Mottin,D.,Lissandrini,M.,Velegrakis,Y.andPalpanas,T.Exemplarqueries:Givemeanexampleofwhatyouneed. PVLDB2014[Jayaram15]Jayaram,N.,Khan,A.,Li,C.,Yan,X.andElmasri,R.Queryingknowledgegraphsbyexampleentitytuples. TKDE,2015

D. MOTTIN, E. MÜLLER 160

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KDD 2018 TUTORIAL

References

[Tong06]H.Tong&C.Faloutsos:Center-PieceSubgraphs:ProblemDefinitionandFastSolutions.(KDD2006)[Staudt14]C.Staudt,Y.Marrakchi,H.Meyerhenke:DetectingCommunitiesAroundSeedNodesinComplexNetworks(BigData 2014)[Epasto15]Epasto etal.Ego-NetCommunityMiningAppliedtoFriedSuggestion.(VLDB2015)[Iglesias14]Iglesiasetal.LocalContextSelectionforOutlierRankinginGraphswithMultipleNumericNodeAttributes(SSDBM2014)[Iglesias13]Iglesiasetal.StatisticalSelectionofCongruentSubspacesforMiningAttributedGraphs(ICDM2013)[Perozzi14]Perozzi etal.FocusedClusteringandOutlierDetectioninLargeAttributedGraphs(KDD2014)

D. MOTTIN, E. MÜLLER 161

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KDD 2018 TUTORIAL

References

[Mottin15]Mottin,D.,Bonchi,F.andGullo,F.GraphQueryReformulationwithDiversity.KDD,2015[Vasilyeva16]Vasilyeva,E.,Thiele,M.,Bornhövd,C.andLehner,W..Answering“WhyEmpty?”and“WhySoMany?”queriesingraphdatabases. JCSS,2016[Ranu14]Ranu,S.,Hoang,M.andSingh,A.Answeringtop-krepresentativequeriesongraphdatabases.SIGMOD,2014[Wu13]Wu,Y.,Yang,S.,Srivatsa,M.,Iyengar,A.andYan,X.Summarizinganswergraphsinducedbykeywordqueries. PVLDB,2013[Fan13]Fan,W.,Wang,X.andWu,Y.Diversifiedtop-kgraphpatternmatching.VLDB,2013[Gupta14]Gupta,M.,Gao,J.,Yan,X.,Cam,H.andHan,J.Top-kinterestingsubgraphdiscoveryininformationnetworks.ICDE,2014[Zou10]Zou,L.,Chen,L.,Özsu,M.T.andZhao,D.Dynamicskylinequeriesinlargegraphs.DASFAA,2010

D. MOTTIN, E. MÜLLER 162


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