Table Of ContentLecture Notes in Social Networks
Reda Alhajj · H. Ulrich Hoppe
Tobias Hecking · Piotr Bródka
Przemyslaw Kazienko Editors
Network
Intelligence
Meets User
Centered Social
Media Networks
Lecture Notes in Social Networks
Serieseditors
RedaAlhajj,UniversityofCalgary,Calgary,AB,Canada
UweGlässer,SimonFraserUniversity,Burnaby,BC,Canada
HuanLiu,ArizonaStateUniversity,Tempe,AZ,USA
RafaelWittek,UniversityofGroningen,Groningen,TheNetherlands
DanielZeng,UniversityofArizona,Tucson,AZ,USA
AdvisoryBoard
CharuC.Aggarwal,YorktownHeights,NY,USA
PatriciaL.Brantingham,SimonFraserUniversity,Burnaby,BC,Canada
ThiloGross,UniversityofBristol,Bristol,UK
JiaweiHan,UniversityofIllinoisatUrbana-Champaign,Urbana,IL,USA
RaúlManásevich,UniversityofChile,Santiago,Chile
AnthonyJ.Masys,UniversityofLeicester,Ottawa,ON,Canada
CarloMorselli,SchoolofCriminology,Montreal,QC,Canada
Moreinformationaboutthisseriesathttp://www.springer.com/series/8768
Reda Alhajj (cid:129) H. Ulrich Hoppe (cid:129) Tobias Hecking
Piotr Bródka (cid:129) Przemyslaw Kazienko
Editors
Network Intelligence Meets
User Centered Social Media
Networks
123
Editors
RedaAlhajj H.UlrichHoppe
DepartmentofComputerScience DepartmentofComputerScienceand
UniversityofCalgary AppliedCognitiveScience
Calgary,Alberta,Canada UniversityofDuisburg-Essen
Duisburg,Nordrhein-Westfalen,Germany
TobiasHecking
ComputerScienceandApplied PiotrBródka
CognitiveScience DepartmentofComputationalIntelligence
UniversityofDuisburg-Essen TheEngineCentre,Facultyof
Duisburg,Nordrhein-Westfalen,Germany ComputerScienceandManagement
WroclawUniversityofScience
PrzemyslawKazienko andTechnology
FacultyofComputerScience Wroclaw,Poland
andManagement
TheEngineCentre
WroclawUniversityofScience
andTechnology
Wroclaw,Poland
ISSN2190-5428 ISSN2190-5436 (electronic)
LectureNotesinSocialNetworks
ISBN978-3-319-90311-8 ISBN978-3-319-90312-5 (eBook)
https://doi.org/10.1007/978-3-319-90312-5
LibraryofCongressControlNumber:2018944599
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Contents
PartI CentralityandInfluence
TargetingInfluentialNodesforRecoveryinBootstrapPercolation
onHyperbolicNetworks......................................................... 3
ChristineMarshall,ColmO’Riordan,andJamesCruickshank
Process-DrivenBetweennessCentralityMeasures........................... 17
MareikeBockholtandKatharinaA.Zweig
Behavior-Based Relevance Estimation for Social Networks
InteractionRelations............................................................. 35
PascalHeldandMitchKöhler
PartII KnowledgeandInformationDiffusion
Network Patterns of Direct and Indirect Reciprocity
inedXMOOCForums .......................................................... 49
OleksandraPoquetandShaneDawson
ExtractingtheMainPathofHistoricEventsfromWikipedia.............. 65
BenjaminCabreraandBarbaraKönig
IdentifyingAcceleratorsofInformationDiffusionAcrossSocial
MediaChannels .................................................................. 83
Tobias Hecking, Laura Steinert, Simon Leßmann, Víctor H. Masías,
andH.UlrichHoppe
PartIII AlgorithmsandApplicationsI
CommunityAliveness:DiscoveringInteractionDecayPatterns
inOnlineSocialCommunities .................................................. 97
MohammedAbufouda
v
vi Contents
ExtendedFeature-DrivenGraphModelforSocialMediaNetworks ...... 119
Ziyaad Qasem, Tobias Hecking, Benjamin Cabrera, Marc Jansen,
andH.UlrichHoppe
IncrementalLearninginDynamicNetworksforNodeClassification...... 133
Tomasz Kajdanowicz, Kamil Tagowski, Maciej Falkiewicz,
andPrzemyslawKazienko
SpongeWalker:CommunityDetectioninLargeDirectedSocial
NetworksUsingLocalStructuresandRandomWalks ...................... 143
OmarJaaforandBabigaBirregah
PartIV AlgorithmsandApplicationsII
MarketBasketAnalysisUsingMinimumSpanningTrees .................. 155
MauricioA.Valle,GonzaloA.Ruz,andRodrigoMorrás
SimulatingTradeinEconomicNetworkswithTrEcSim .................... 169
GabrielBarina,CalinSicoe,MihaiUdrescu,andMirceaVladutiu
Towards an ILP Approach for Learning Privacy Heuristics
fromUsers’Regrets.............................................................. 187
NicolásEmilioDíazFerreyra,ReneMeis,andMarittaHeisel
PartV ContentAnalysis
TrumpVersusClinton:TwitterCommunicationDuring
theUSPrimaries ................................................................. 201
JenniferFromm,StefanieMelzer,BjörnRoss,andStefanStieglitz
StrengthofNations:ACaseStudyonEstimatingtheInfluence
ofLeadingCountriesUsingSocialMediaAnalysis .......................... 219
AlexandruTopîrceanuandMihaiUdrescu
IdentifyingPromisingResearchTopicsinComputerScience .............. 231
RajmundKlemin´skiandPrzemyslawKazienko
Index............................................................................... 243
Part I
Centrality and Influence
Targeting Influential Nodes for Recovery
in Bootstrap Percolation on Hyperbolic
Networks
ChristineMarshall,ColmO’Riordan,andJamesCruickshank
Abstract The influence of our peers is a powerful reinforcement for our social
behaviour,evidencedinvoterbehaviourandtrendadoption.Bootstrappercolation
is a simple method for modelling this process. In this work we look at bootstrap
percolationonhyperbolicrandomgeometricgraphs,whichhavebeenusedtomodel
the Internet graph, and introduce a form of bootstrap percolation with recovery,
showing that random targeting of nodes for recovery will delay adoption, but this
effectisenhancedwhennodesofhighdegreeareselectivelytargeted.
Keywords Bootstrappercolation · Bootstrappercolationwithrecovery ·
Hyperbolicrandomgeometricgraphs
1 Introduction
Inthiswork,weexamineagent-basedmodellingofBootstrapPercolationonHyper-
bolic Random Geometric graphs, and introduce a modified version of Bootstrap
Percolation with Recovery on the same set of graphs. Bootstrap percolation has
been used to model social reinforcement, with the rationale that individuals are
more likely to adopt a new technology, for example, if a significant number of
theircontactsalreadyusethattechnology.Akeyquestioninbootstrappercolation
is whether the activity will percolate or not. Our focus is on the effects of
spatial structure on the spread or percolation of an activity, specifically observing
bootstrap percolation on hyperbolic random geometric graphs. Our motivation for
using hyperbolic networks is they share many features with real-world complex
C.Marshall((cid:2))·C.O’Riordan
DisciplineofInformationTechnology,NationalUniversityofIreland,Galway,Ireland
e-mail:[email protected];[email protected]
J.Cruickshank
SchoolofMathematics,NationalUniversityofIreland,Galway,Ireland
e-mail:[email protected]
©SpringerInternationalPublishingAG,partofSpringerNature2018 3
R.Alhajjetal.(eds.),NetworkIntelligenceMeetsUserCenteredSocialMedia
Networks,LectureNotesinSocialNetworks,
https://doi.org/10.1007/978-3-319-90312-5_1
4 C.Marshalletal.
networks;typicallydisplayingpower-lawdegreedistribution,highclustering,short
pathlengths,highclosenessandsignificantbetweennesscentralisation.
We have previously created a set of hyperbolic random geometric graphs from
unconnected, with increasing edge density, to fully connected. In this work, we
attach a population of agents to the graphs and explore the effect of percolation
processesontheseagentsonthissetofgraphs.Ourfirstquestioniswhetherwecan
identifyadistinctpercolationthresholdasweincreasethenumberofedges,above
whichtheactivitypercolatesandbelowwhichtheactivityfailstopercolate.
Typically work on bootstrap percolation has looked at the effect of the initial
active seed seton the final outcome of the process, looking atthe choice of active
seed, or at the fraction of active seeds required to guarantee percolation of the
activity. This has been used in, for example, viral marketing to see which nodes
might best be targeted to optimise the spread of information. Our overall interest
is in targeting nodes which might prevent the percolation of the activity, given a
random seed set of fixed size. This could model a situation where a network is
randomlytargetedwithactiveseeds,andwewishtoknowwhichnodesmighthave
thebestchanceofobstructingtheprocess.Ourfocusisonminimisingthespreadof
anactivitygivenasmall-scaleattackatrandompointswithinthenetwork.
We develop a modified version of standard bootstrap percolation which allows
for the recovery of a defined percentage of active nodes after each activation step;
byrecoverywemeanthetransitionfromactivestatebacktoinactivestate.Ournext
questionsarewhetherthiswillimpactthespreadoftheactivity,and,furthermore,
ifweselectivelytargetactivenodesofhighdegree,willthishaveagreaterimpact
onthespreadofactivitythanthesamepercentagechosenatrandom,giventhathub
nodesarecommonlyregardedasinfluentialnodesinanetwork.
Section 2 provides related information about bootstrap percolation and hyper-
bolic graphs. Section 3 introduces our conceptual framework for Bootstrap Perco-
lation with recovery. Section 4 describes our experimental set-up, and our results
are presented in Sect.5. Our conclusions are discussed in Sect.6, together with
suggestionsforfuturework.
2 Background
Inthissectionwedescribebootstrappercolationandhyperbolicrandomgeometric
graphs.
2.1 BootstrapPercolation
Bootstrap percolation is a dynamic process in which an activity can spread to
a node in a network if the number of active neighbours of that node is greater
than a predefined activation threshold. This process can model forms of social
