From Security to Community Detection in Social Networking Platforms PDF
Preview From Security to Community Detection in Social Networking Platforms
Lecture Notes in Social Networks Panagiotis Karampelas Jalal Kawash Tansel Özyer Editors From Security to Community Detection in Social Networking Platforms 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 Panagiotis Karampelas (cid:129) Jalal Kawash Tansel Özyer Editors From Security to Community Detection in Social Networking Platforms 123 Editors PanagiotisKarampelas JalalKawash DepartmentofInformatics&Computers DepartmentofComputerScience HellenicAirForceAcademy UniversityofCalgary Dekelia,Greece Calgary,AB,Canada TanselÖzyer DepartmentofComputerEngineering TOBBUniversityofEconomics andTechnology Ankara,Turkey ISSN2190-5428 ISSN2190-5436 (electronic) LectureNotesinSocialNetworks ISBN978-3-030-11285-1 ISBN978-3-030-11286-8 (eBook) https://doi.org/10.1007/978-3-030-11286-8 LibraryofCongressControlNumber:2019935126 ©SpringerNatureSwitzerlandAG2019 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG. Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Introduction This volume is a compilation of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM-2017), held in Sydney, Australia, August 2017. The authors of the selected papers were kindly asked to provide extended versions of the papers that werethensubjectedtoanadditionalrefereeingprocess.Withinthebroadercontext ofonlinesocialnetworks,thevolumefocusesonimportantandstate-of-the-artwork intheareaofdetectionandpredictiontechniquesusinginformationfoundgenerally ingraphsandparticularlyinsocialnetworks. FromSecuritytoCommunityDetectioninSocialNetworking Platforms Social networks have not only been under the light due to the vast participation of users but also because they are a very reliable source of information either for collectingsecurity-relatedinformation,suchasmaliciousIPaddresses,ordetecting onlinecommunitiesindiversecontextsusinginnovativetechniques,suchasgenetic algorithms or surface tension analysis. Additionally, the absence of authentic data due to privacy concerns is addressed. This absence complicates testing novel methodologiesinvariousareassuchasfinancialanalysisorminingofunstructured data. Two other chapters present solutions for creating synthetic though realistic data for the aforementioned cases. The rest of the chapters presented in this book propose innovative methods to explore graphs and social networks in an attempt to provide timely, reliable, and useful results in a continuously increasing data- intensiveenvironment.Thevarietyofapproachesadoptedintheresearchpresented in the book demonstrates the diversity of the application contexts and can act as a source for further research not only in the designated areas presented but in other areasofapplication. v vi Preface Thefirstchapterexamineshowsocialnetworkanalysiscanbeappliedinacom- plex environment where numerous actors are involved in preserving biodiversity in a protected area. By applying social networking metrics in two ego networks, the authors study the role of the management actors for the preservation of the area at stake and how these metrics can assist in improving the cooperation of environmentalconservationinNatura2000areas. Inthesecondchapter,theauthorsproposeanovelmethodologytodetectsocial network communities without estimating the number of communities beforehand using a modified genetic algorithm. The reported results have shown that the proposedmethodologyperformsverywelleveninlargedatasets. Inthethirdchapter,theauthorsrecognizingtheproliferationofthemultidimen- sionalityofcurrentsocialnetworksproposeanovelMultidimensionalCommunities Detection Algorithm that is capable of handling outliers and at the same time is able to detect multidimensional communities. The proposed technique can also automatically unfold the hidden communities in a multidimensional context, by creatinganovelpropagationrulethatexploitsthemostfrequentlyusedinteraction dimensionsamongneighborsasanadditionalconstraintformembershipselections. Thefourthchapterproposestwomethodsfordetectinglocalcommunities.The firstoneproposesfindingderivativesingraphspaceand,asaresult,takesadvantage ofderivative-basedmethodsintographtheory.Thesecondisinspiredbytheactive contour algorithm in computer vision and explores the concepts of curvature and gradient of the community’s boundary. The proposed methods are enhanced by applyingtheprinciplesofsurfacetensionfromchemistryindynamicnetworksby addingnewnodes.Theexperimentspresentedprovidepromisingresultsregarding theperformanceoftheproposedmethods. In the fifth chapter, the authors propose a new graph embedding approach for attributed graph clustering since nowadays rich and heterogeneous attribute informationhasbecomewidelyavailableespeciallyinsocialnetworksuserprofiles. Byapplyingtheproposedmethodology,theauthorsdemonstratethatitispossible to transform the challenging attributed graph clustering problem into a multi- dimensional data clustering problem. This transformation outperforms traditional attributedgraphclusteringtechniquesintermsofeffectivenessandefficiency. The sixth chapter elaborates on the problem of big graph analytics in dynamic graphs. Traditional methods rely on tracking the added or the removed nodes in a graph. The proposed technique takes advantage of additional information that is available by creating the Edge Sample and by employing the discard algorithm, whichgeneratesanunbiasedestimateofthetotalnumberoftrianglesthatmayneed to be updated due to dynamic changes. The proposed method is evaluated against traditionalmethodsprovidingpromisingresults. Intheseventhchapter,theauthorsfocusontheproblemofsemi-structuredand structureddatathatareusedindecision-making.Eventhoughthereareseveraldata qualitymanagementapproaches,itisnotalwaysfeasibletocompareorassessthe performanceofthespecificapproachessincetherearenopublicdatasetsavailableto beusedforsuchpurpose.Thechapteraddressesthespecificchallengebyproposing a system that is able to produce synthetic semi-structured and structured data Preface vii satisfying a set of integrity constraints to be used for the assessment of the data qualitymanagementmethods. In the eighth chapter, the authors propose a novel methodology for randomly generating entire financial systems while diagnosing the absence of real financial trade datasets for analyzing the impact of financial regulation concerning the collateralization of derivative trades. Based on a novel open-source risk engine, the authors enable data scientists to apply diverse techniques such as data mining, anomalydetection,andvisualizationtorunsimulations. Theninthchapteridentifiestheneedforminingunstructuredinformation,such asinhackers’forums,andproposesanovelmethodologyandacorrespondingtool based on matrix decomposition method to extract latent features of the behavioral information of the users. These features are then used along with some keywords from any language to classify malicious IP addresses found in the forums. Based ontheexperimentalanalysis,theauthorsareabletodetectuptothreetimesmore maliciousIPaddressthantheblacklistofVirusTotal. The tenth and final chapter proposes a method for detecting topic changes between different time periods. The proposed method is based on two techniques: one is from an information-theoretic analysis of the terms distributions, and the secondisbasedondocumentclusteringinperiodsunderreview.Thevalidityofthe proposedmethodistestedagainstvariousTwitterdatasets. Dekelia,Greece PanagiotisKarampelas Calgary,AB,Canada JalalKawash Ankara,Turkey TanselÖzyer Contents Real-World Application of Ego-Network Analysis to Evaluate EnvironmentalManagementStructures ...................................... 1 Andreea Nita, Steluta Manolache, Cristiana M. Ciocanea, andLaurentiuRozylowicz AnEvolutionaryApproachforDetectingCommunitiesinSocial Networks.......................................................................... 17 KorayOzturk,FarukPolat,andTanselÖzyer OnDetectingMultidimensionalCommunities................................ 45 AmaniChouchane,OualidBoutemine,andMohamedBouguessa DerivativesinGraphSpacewithApplicationsforFinding andTrackingLocalCommunities.............................................. 79 M.AminRigi,IreneMoser,andM.MehdiFarhangi GraphClusteringBasedonAttribute-AwareGraphEmbedding.......... 109 EsraAkbasandPeixiangZhao OnCountingTrianglesThroughEdgeSamplinginLarge DynamicGraphs ................................................................. 133 GuyueHanandHarishSethu GenerationandCorruptionofSemi-StructuredandStructuredData .... 159 SamirAl-janabiandRyszardJanicki ADataScienceApproachtoPredicttheImpactofCollateralization onSystemicRisk ................................................................. 171 SharynO’Halloran,NikolaiNowaczyk,DonalGallagher, andVivekSubramaniam ix x Contents MiningActionableInformationfromSecurityForums:TheCase ofMaliciousIPAddresses....................................................... 193 JoobinGharibshah,TaiChingLi,AndreCastro,KonstantinosPelechrinis, EvangelosE.Papalexakis,andMichalisFaloutsos TemporalMethodstoDetectContent-BasedAnomalies inSocialMedia ................................................................... 213 JacekSkryzalin,RichardFieldJr.,AndrewFisher,andTravisBauer Index............................................................................... 231
The list of books you might like
