Complex Networks: Results of the 2009 International Workshop on Complex Networks (CompleNet 2009)

231 Pages·2009·16.272 MB·English

by  Giorgio Fagiolo, Javier Reyes, Stefano Schiavo (auth.), Santo Fortunato, Giuseppe Mangioni, Ronaldo Menezes, Vincenzo Nicosia (eds.)

SantoFortunato,GiuseppeMangioni,RonaldoMenezes,
andVincenzoNicosia(Eds.)
ComplexNetworks
StudiesinComputationalIntelligence,Volume207
Editor-in-Chief
Prof.JanuszKacprzyk
SystemsResearchInstitute
PolishAcademyofSciences
ul.Newelska6
01-447Warsaw
Poland
E-mail:[email protected]
Furthervolumesofthisseriescanbefoundonour Vol.196.ValentinaEmiliaBalas,Ja´nosFodorand
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Iterative-InterpolationSuper-ResolutionImage Vol.207.SantoFortunato,GiuseppeMangioni,
Reconstruction: RonaldoMenezes,andVincenzoNicosia(Eds.)
AComputationallyEfficientTechnique,2009 ComplexNetworks,2009
ISBN978-3-642-00384-4 ISBN978-3-642-01205-1
Santo Fortunato,Giuseppe Mangioni,
Ronaldo Menezes,andVincenzo Nicosia (Eds.)
Complex Networks
Results of the 2009 InternationalWorkshop
on Complex Networks (CompleNet 2009)
C
123
SantoFortunato RonaldoMenezes
ComplexNetworksLagrangeLaboratory FloridaInstituteofTechnology
ISIFoundation DepartmentofComputerSciences
VialeS.Severo65 150W.UniversityBlvd
10133Torino MelbourneFL32901
Italy USA
E-mail:[email protected] E-mail:rmenezes@cs.fit.edu
GiuseppeMangioni VincenzoNicosia
DipartimentodiIngegneria LaboratoriosuiSistemiComplessi
InformaticaedelleTelecomunicazioni ScuolaSuperiorediCatania
Universita`degliStudidiCatania ViaS.Nullo5/i–95123Catania
VialeAndreaDoria,6 Italy
I95125Catania E-mail:[email protected]
Italy
E-mail:[email protected]
ISBN 978-3-642-01205-1 e-ISBN978-3-642-01206-8
DOI 10.1007/978-3-642-01206-8
Studiesin Computational Intelligence ISSN1860949X
Library of Congress Control Number:Applied for
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Editorial
SantoFortunatoandRonaldoMenezes
Complex Networks
Complexityscienceisamajorscientificrevolutionofourtime.Asystemiscomplex
if its macroscopic behavior cannot be simply reduced to the microscopic behav-
ior of its constituents. Nonlinear dynamics and feedback effects are the dominant
characters of the dynamics of complex systems. A major contribution of the last
years is the discovery that many complex systems in biology, physics, social and
computersciences,canbestudiedthroughtheirgraph(network)representation,in
which the elementaryunits of a system become nodesand their interactionslinks
connectingnodes[1,2,3,5,4,6].Examplesaresocialnetworksofacquaintances,
protein-proteininteraction networks, food webs, the Internet and the World Wide
Web.Networksrepresentingrealsystemsarecharacterizedbyasetofcommonfea-
tures,whichmarkedlydifferfromfeaturesofsimplelatticesorrandomgraphs.This
is why one refers to them as complex networks. The most striking feature is the
factthatthedistributionofthenumberofneighborsofanode(degree)istypically
broad, with a tail that often follows a power law [7]. This means that nodes with
lowdegreecoexistwith nodeswithlargedegree,orhubs.Thepresenceofhubsis
responsibleforanumberofremarkablefeaturesofcomplexnetworks,liketheirre-
silience againstrandomattacks or failures[8], and the absence of percolationand
epidemic thresholds [8, 9]. Another importantcharacteristic of complexnetworks
concerns clustering, i.e. the probability that neighbors of a node are themselves
neighbors[10].Realnetworksusuallydisplayhighvaluesforthisprobability,much
higherthaninrandomgraphs,andthispropertyplaysanimportantroleinspreading
SantoFortunato
ISIFoundation,VialeS.Severo65,10133Torino,Italy.
e-mail:[email protected]
RonaldoMenezes
Bio-InspiredComputingLab,DepartmentofComputerSciences,FloridaInstituteof
Technology,Melbourne,Florida,USA.
e-mail:[email protected]
VI Editorial
phenomena. Finally, complex networks are characterized by short geodesic paths
betweennodes,i.e.itispossibletoreacheverynodefromanyothernodeinasmall
numberofsteps,thatgrowsonlylogarithmicallywiththesizeofthesystem(small-
world property) [10]. The small-world property holds as well for simple random
graphs,butincomplexnetworksgeodesicpathsaremuchshorter,duetothepres-
enceofthehubs,whicharefundamentalcommunicationstations.
The main issues in the theoryof complexnetworksconcerntheirstructure and
their dynamics. A lot of work has been devoted to describe the structure of com-
plexnetworks.Localpropertieslike degree,clusteringcoefficient,nodecentrality,
etc.havebeenstudied,alongwiththeirstatisticaldistributions.Moreover,complex
networksdisplayinterestingstructuresatthemesoscopiclevel,consistingingroups
ofnodeswithmanylinksbetweennodesofthesamegroupandcomparativelyfew
betweennodesofdifferentgroups[11].Suchgroupsofnodesarecalledcommuni-
ties,andrepresentanessentialaspectoftheorganizationofnetworks,whichallow
toinfernontrivialrelationshipsbetweenthenodes.
Asfarasthedynamicsisconcerned,onehastodistinguishthedynamicsofcom-
plex networks, from the dynamics of processes taking place on them. Models of
networkdynamicshavebeenproposedsincetheoriginsofcomplexnetworks.They
aremostlymodelsofgrowth,inwhichnodesareiterativelyaddedtoasmallinitial
core. The fat-tailed distributions of degree observed in real systems are produced
by the tendency of nodesto set links with nodeswith large degree [12], although
severalothermechanismshavebeenproposed.Studyingprocessestakingplaceon
networks is essential to understand how networks function. Many traditionalpro-
cesses,likepercolation,spinmodelsdynamics,synchronization,opiniondynamics,
epidemic spreading, etc. have been thoroughly analyzed, highlighting the role of
hubsasfundamentalactorsinsuchprocesses.
Nowadaysnetworktheoryisratherdeveloped.Still,thereareseveralopenprob-
lemsandtheresearchactivityinthisfieldremainsintense.Forinstance,theproblem
of detecting communities in networks is still far from being solved and it is now
probablythemostactiveresearchtopicinthearea.Similarly,alotofefforthasbeen
putinthestudyofweightednetworks,whereoneisconfrontedwiththeissueofthe
interplaybetweenthestrengthoftheinteractionsandthenetworktopology.Finally,
arecentresearchdirectionfocusesontheissueofthecoevolutionofnetworksand
dynamics,i.e.withtheissueoftheinterplaybetweentheevolutionofthenetwork
andthatofdynamicalprocessestakingplaceonit,whenbothevolutionsoccuron
comparativetimescales.
This volume aims at increasingour knowledgeon network theoryand applica-
tionsbydiscussingsomeveryrecentresultsinthefield.Theenclosedpaperscover
many of the issues above mentioned, and were contributed by a widely interdis-
ciplinarycommunityofscientists, includingphysicists, mathematicians,computer
sciences,engineersandeconomists.Wedistinguishfivesubjectareasforthe18pa-
pers of this volume: analysis of real networks (3 papers), community structure (4
papers), network modeling (3 papers), network dynamics (3 papers) and applica-
tions(5papers).
Editorial VII
AnalysisofRealNetworks
The paper by Fagiolo, Reyes and Schiavo deals with the InternationalTrade Net-
work,i.e.thenetworkofimport-exportrelationshipsbetweencountries.Theauthors
analyzedthemostimportantstatisticsofnetworkpropertiesinatimespanoftwenty
yearsandfoundthatthenode-statisticdistributionsarestable,whereasthedistribu-
tionofpositivelinkweightsisturningfromalognormaltoapowerlaw.Thepaper
by Nunnari, Puglisi, Bonforte and Spata presents a study of a network built from
theanalysisofplanetarycorrelationsbetweentheactivitiesofvolcanosoverthelast
two thousandyears.Thenetworkdisplaysboththesmall-worldpropertyandhigh
clustering coefficient. Last, the paper by Z˘ivkovic´,Mitrovic´ and Tadic´ focuses on
thenetworkderivedfromcorrelationsinthetimeseriesofgeneexpressionsofyeast.
Modulesofgenescanbeidentifiedbyanalyzingtheeigenvectorsoftheadjacency
matrix,whichisobtainedfromthecorrelationmatrixthroughstandardfiltering.
CommunityStructure
ThepaperbyCristino,AndradeandCosta,likepaperbyFagioloetal.,dealswith
thegeneinteractionsofyeast.Thegoalistoidentifythemodularstructureingene
transcriptionnetworks.Communitiesarefoundwiththemodularityoptimizational-
gorithmbyClausetetal.[13].Structurallysimilarcommunitiestendtosharefunc-
tionalproperties.ThepaperbyGregoryproposesanewmethodtofindoverlapping
communitiesinnetworks.Itconsistsoftwosteps:first,thenetworkistransformedin
anewonewherenodessharedbetweenmodulesaresplit,i.e.representedinmultiple
copies;second,atraditionalcommunitydetectionmethodisappliedtotheresulting
network.ForthesecondphaseGregoryadoptedseveralalgorithms,basedonmod-
ularityoptimizationandrandomwalks.Thetwo-stepstrategyyieldsmoreaccurate
resultsthanspecializedmethodstofindoverlappingcommunities,liketheC-finder
byPalla etal. [14]. In nextpaper,Fiumicello,Longheuand Mangioniintroducea
fastimplementationofacommunitydetectionmethodrecentlyproposedbyLanci-
chinettietal.[15],whichconsistsinthelocaloptimizationofafitnessfunctionwith
the possibility to find overlapsbetween clusters. The gain in speed is obtained by
portingtheoriginalalgorithmonagridenvironment.Inthelastpaperinthisgroup,
Xiang,ChenandZhouintroducedanagglomerativecommunitydetectionmethod,
basedonameasurequantifyingthestructuralsimilaritybetweengraphs.Theproce-
dureismuchfasterthanthefastmethodofClausetetal.[13],basedongreedymod-
ularityoptimization.Ahybridstrategy,combiningthenewmethodwiththemethod
byClausetetal.yieldsbetterestimatesforthemaximummodularityofnetworks.
NetworkModeling
The first paper in this group, by Brandes, Lerner, Nagel and Nick, introduces a
methodtoidentifystructuraltrendsinnetworkensembles.Theensemblesaremix-
turesofplantedpartitionmodels,i.e.classesofgraphswithwelldefinedgroupsof
nodes. The method relies on the relative comparison of the vectors whose com-
ponents are the eigenvalues of the adjacency matrix of graph instances of the
VIII Editorial
ensembles. The paperby Gustedtproposesmodelsthatgenerategraphswith high
clusteringcoefficient.Themodelsareageneralizationoftherecipesbehindthecon-
structionofthe randomgraphsa´ la Erdo¨sandRe´nyi[16]andscale-freenetworks
a´ laBaraba´si-Albert[12].ThepaperbyVillasBoas,RodriguesandCostasuggests
a modelofhighwaynetworks,whichgeneralizesthe conceptof geographicalnet-
works, in that new nodesare introducedbetween pairs of importantnodes, which
simulatesthepresenceofsmallintermediatetownsbetweenimportantcenters.The
modelis ableto describethe UShighwaynetworkbetterthantraditionalcomplex
networksmodels.
NetworkDynamics
This group starts with the paper by Maletic´ and Rajkovic´ where they introduce a
generalmodelforopiniondynamicsthatcombinestwootherpopulardynamics.At
variancewithtraditionalmodels,herenewopinionsareformed.Thedynamicsruns
on different types of scale-free networks, and the final opinion state strongly de-
pendson whetherthe network has a modularstructure or not. The paper by Loya
andLucasprovesanalyticallyresultsonthe stationarystate of a generalepidemic
model,includingasspecialcasespopularmodelsliketheSIS(susceptible-infected-
susceptible) model. The dynamics runs on networks with degree-degree correla-
tions. The epidemic threshold is also rigorously defined. The paper authored by
Melnik and Gleeson, presents an analytical approach to determine the thresholds
and the sizes of the giant components for bond percolation on random networks
withnon-zeroclustering.Thisstudysuggeststhat,foranetworkwithagivenlevel
ofclustering,thepercolationthresholdmaybehigherorlowerthanthethresholdon
a randomized(unclustered)network with the same degreedistribution, depending
onthedegreedistribution.
Applications
The first paper of the application group, authored by Funabashi, Chavalarias and
Cointet, studied the correlations between variables on network nodes beyond the
classic dyadicinteractions.Theyuse informationgeometryto decomposecorrela-
tionsintodifferentordersofstatistics. Highordercorrelationsareused toanalyze
networksofwordoccurrencesderivedfrompoliticalweblogdata. Inthe paperby
Hoche,HardcastleandFlach,theyintroduceamethodtoreducethecomputational
complexityoftopicpredictioninco-authorshipgraphs.Themethodconsistsinre-
ducingthenumberoflinksofthegraph,byusingthepublicationdatesofthepapers.
Testsonrealdatasetsshowthatthemethodisabletorecoverthesamepredictions
as for the full graph, even when a large fraction of links is removed, with a gain
in computationalefficiencyof oneorderof magnitude.The groupfollowwith the
paperbyAoyama,Saito,YamadaandUeda,whichpresentsagraph-basedapproach
forafastsearchofthemostsimilarobjecttoagivenqueryobject.Theapproachis
basedonthefactthatsmall-worldnetworksare“searchable”,inthatanynodecan
reachanytargetnodeusingonlylocalinformation.Thesimilaritysearchisturned
into a search problem on the similarity network of objects. The method performs
Editorial IX
wellwiththegainofalowexpectedsearchcost.ThepaperbyLawniczak,Wuand
DiStefanodealswiththeproblemofnetworksecurity,byfocusingonthedetection
of anomalous traffic packets in networks like the Internet. They use information
entropyto monitorpacket traffic, and find that the entropyprofiles of packet traf-
fic at selected nodes/routers may indeed give information on the traffic behavior
over the whole network, and revealanomalies due to distributed denial-of-service
attacks.Finally,inthepaperbyCollingsworthandMenezes,atemporalanalysisof
the email network of Enron employees is performed,with the goal of identifying
propertiesofthenetworkthatanticipatetheoccurrenceofsocialtensionsbetween
people.In particular,theyfindthatan increasein the numberoftightlyconnected
groupsseemstobeasignalofsocialtension.
Acknowledgements. We would like to thank Springer for the publication of this
volume.Wearealsoverygratefultoalltheauthorsofpaperscontainedinthevol-
umeandto the membersof CompleNet2009ProgramCommittee,whose activity
hasbeeninvaluabletoguaranteeacarefulscreeningofthesubmittedcontributions.
References
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CompleNet
Workshop on Complex Networks
Welcome to Catania, Italy, for the first International Workshop on Complex Net-
works(CompleNet2009).Thisworkshopserieswasaninitiativeofresearchgroups
from: Dipartimento di Ingegneria Informatica e delle Telecomunicazioni(DIIT) -
UniversityofCatania-Italy,theLaboratoriosuiSistemiComplessi–ScuolaSuperi-
orediCatania,Italy,andtheBio-InspiredComputingLaboratoryintheDepartment
ofComputerSciences,FloridaInstituteofTechnology,USA.CompleNet2009was
hosted by the DIIT, EngineeringFaculty, University of Catania, Italy during May
26–27,2009.
CompleNet aims at bringing together researchers and practitioners working on
areas related to complex networks into an intensive 2 days high quality program.
Despite the existence of other meetings, this is the first attemptto bring Complex
NetworkresearchersintoaformatlongknownbytheComputerScienceandEngi-
neeringcommunitiesbutrelativelyuncommonforthe Sciences(Physics,Biology,
SocialSciences,etc.).ThemaindifferenceliesonthefactthatCompleNetrequires
full papers to be submitted and not only abstracts. We believe this improves the
qualityoftheworkshopandthisbookisproofofthis.
Inthepasttwodecadeswehavebeenwitnessinganexponentialincreaseonthe
numberofpublicationsinComplexNetworks.Frombiologicalsystemstocomputer
science,fromeconomictosocialsystems, complexnetworksarebecomingperva-
siveinmanyfieldsofscience.Itisthisinterdisciplinarynatureofcomplexnetworks
thatthisworkshopaimsataddressingaswebelievethereisstillalongfruitfulway
forustoexplore.Theexchangeofideasbetweentheareashelpsintheacceleration
ofthisprocess.
Wewouldliketothankfirstalltheauthorsandparticipantsoftheworkshop;they
arethemainreasonthiseventwasasucess.Puttingaworkshoptogetherrequiresa
lotofworkanddedicationparticularlywhenwewantedtomakesuretheresults(this
book)would stand on its own. We are particularly gratefulto the ProgramChairs
Santo FortunatoandRonaldo Menezesfortheir many contributionsand extensive
worktowardsputtingtogetheraverysuccessfulprogram.Wewouldliketothankthe
memberstheProgramCommitteefortheirdedicationandinvaluableexpertreviews,
ensuringa high-qualitytechnicalprogram.We are also particularlygratefulto the