Hybrid Intelligence for Social Networks PDF
Preview Hybrid Intelligence for Social Networks
Hema Banati Siddhartha Bhattacharyya Ashish Mani · Mario Köppen Editors Hybrid Intelligence for Social Networks Hybrid Intelligence for Social Networks Hema Banati • Siddhartha Bhattacharyya (cid:129) Ashish Mani (cid:129) Mario KoRppen Editors Hybrid Intelligence for Social Networks 123 Editors HemaBanati SiddharthaBhattacharyya Dept.ofComputerScience Dept.ofInformationTechnology DyalSinghCollege RCCInstituteofInformationTechnology UniversityofDelhi Kolkata Delhi,India WestBengal,India AshishMani MarioKoRppen Dept.ofElectrical&Electronics NetworkDesignandResearchCenter Engineering KyushuInstituteofTechnology AmityUniversity Fukuoka,Japan Noida UttarPradesh,India ISBN978-3-319-65138-5 ISBN978-3-319-65139-2 (eBook) DOI10.1007/978-3-319-65139-2 LibraryofCongressControlNumber:2017959952 ©SpringerInternationalPublishingAG2017 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,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Dedication Dr.Hema Banatiwouldliketodedicatethe bookto herfirstsocialnetworkon thisearth: herprecious family! Prof.(Dr.)SiddharthaBhattacharyyawould liketo dedicatethisbooktohislatefather, AjitKumarBhattacharyya,hislatemother HashiBhattacharyya,hisbelovedwife Rashniand hiscousinbrothersSubrata, Bishwarup,SomenathandDebasish Dr.AshishManiwouldliketo dedicatethis bookto MostRevered Dr.Prem Saran Satsangi,ChairmanAdvisoryCommitteeon EducationDayalbagh Prof.(Dr.)MarioKoeppen wouldliketo dedicatethisbooktoProf.(Dr.) Albert-LászlóBarabási,apioneer innetwork theoryresearch. Preface The field of social networks has assumed paramount importance because of its omnipresence in our daily lives. As social networks have become the backbone of our society, this book is aimed at bringing under a common umbrella the different dimensions/perceptions of social networks varying from applications to the development of new artificial intelligent techniques for social networks to understandingtheimpactofsocialnetworksine-commerce. The traditionalway of handlingthese networkswas focussed on three primary areas (cid:129) Conceptual:Mathematicalmodels,novelalgorithms,frameworks,etc. (cid:129) Analytical:Trendanalysis,dataanalysis,opinionanalysis,datamining,knowl- edgeextraction,etc. (cid:129) Technological:Tools,innovativesystems,applications,etc. Recent focus has shifted to the application of artificial intelligence techniques for understanding social networks on the Web. These help in the handling of the shortcomings and limitations associated with the classical platforms of computa- tion, particularly for handling uncertainty and imprecision. Soft computing as an alternativeandextendedcomputationparadigmhasbeenmakingitspresencefelt. Accordingly,aphenomenalgrowthofresearchinitiativesinthisfieldisevident.Soft computing techniques include the elements of fuzzy mathematics, primarily used forhandlingvariousreal-lifeproblemsengrossedwithuncertainty;theingredients ofartificialneuralnetworks,usuallyappliedtocognition,learningandsubsequent recognitionbymachines,therebyinducingtheflavourofintelligenceinamachine through the process of its learning; and components of evolutionary computation mainly used for searching,explorationand the efficient exploitationof contextual informationandknowledge,whichareusefulforoptimization. Individually,thesetechniqueshavegottheirstrongpointsin additiontolimita- tions.Inseveralreal-lifecontexts,ithasbeenobservedthattheyplayasupplemen- taryroleforoneanother.Naturally,thishasgivenrisetoseriousresearchinitiatives for exploring avenues of hybridization of the above-mentioned soft computing techniques. This resulted in more robust and intelligent solutions in the form of vii viii Preface neuro-fuzzy, fuzzy-genetic, rough-neuro, rough-fuzzy, neuro-fuzzy-genetic, neuro- fuzzy-rough, quantum-neuro-fuzzy architectures. Interestingly, the scope of such hybridizationisgraduallyfoundtobeall-encompassing. The book provides a multidimensional approach to social networks. The first two chapters deal with the basic strategies for social networks, such as mining text from such networks or applying social network metrics using a hybrid approach, the next six focus on the prime research areas in social networks: community detection, influence maximization and opinion mining. The chapters “CommunityDetectionUsingNature-InspiredAlgorithms,”“AUnifiedFramework for Community Structure Analysis in Dynamic Social Networks,” “GSO-Based Heuristics for Identification of Communities and Their Leaders,” and “A Holistic Approach to Influence Maximization” present varied intelligent approaches to detecting communities and maximizing influence through evolutionary and other hybridtechniques.Thechapters“OpinionDynamicsThroughNaturalPhenomenon of Grain Growth and Population Migration” and “Opinion Mining from Social Travel Networks” focus on mining opinions from social networks. The last five chaptersofthebook,“FacilitatingBrandPromotionThroughOnlineSocialMedia: A Business Case Study,” “Product Diffusion Pattern Analysis Model Based on Users’ Reviews of E-commerce Applications,” “Hierarchical Sentiment Analysis Model for Automatic Review Classification for E-commerce Users,” “Trends and PatternAnalysisinSocialNetworks,”and“ExtensiblePlatformofCrowdsourcing onSocialNetworkingSites:AnAnalysis,”concentrateonstudyingtheimpactand useofsocialnetworksinsociety,primarilyineducation,thecommercialsectorand theupcomingtechnologyofcrowdsourcing. The book begins with a study of the hybrid techniques used to mine textual data from social networks and media data in the chapter “Hybrid Intelligent TechniquesinTextMiningandAnalysisofSocialNetworksandMediaData”. It considers the fact that social networks are considered to be a profuse source of viewpoints and an enormous amount of social media data is produced on a regular basis. This chapter presents a detailed methodology on how data mining, especiallytextmining,isappliedtosocialnetworksingeneral.Therelevanceofthis isessentialbecauseofthehugeamountofdatageneratedfromthecommunication between users signed up for the various social media platforms on several topics such as books, movies, politics, products, etc. The users vary in terms of factors such as viewpoints,scenarios,geographicalsituations, andmany othersettings. If minedefficiently,thereisthepotentialtoprovideahelpfuloutcomeofanexegesis of the social quirks and traits. Furthermore, it introduces the traditional models used in mining the various hybrid methodologies that have evolved and provides acomparativeanalysis. Basic parametersfor social networkanalysis are socialnetwork metrics. There are numerous social network metrics. During the data analysis stage, the analyst combinesdifferentmetrics in the search for interesting patterns. This process can be exhaustive with regard to numerous potential combinations and how we can combine different metrics. On the other hand, other non-network measures can be observed along with social network metrics. The chapter “Social Network Preface ix Metrics Integration into Fuzzy Expert Systems and Bayesian Networks for Better Data Science Solution Performance” proposes a methodology on fraud detection systems in the insurance industry, where a fuzzy expert system and the Bayesian network were the basis for the analytical platform, and social network metrics were used as part of the solution to improve performance. The solution thatwasdevelopedshowstheimportanceofintegratedsocialnetworkmetricsasa contributiontobetteraccuracyinfrauddetection. Community detection in social networks has become a dominating topic of current data research as it has a direct implication for many different areas of importance,whethersocialnetworks,citationnetworks,trafficnetworks,metabolic networks,protein-proteinnetworksorwebgraphs,etc.Miningmeaningfulcommu- nitiesin a real-worldnetworkisa hardproblembecauseof thedynamicnatureof thesenetworks.Theexistingalgorithmsforcommunitydetectiondependchieflyon thenetworktopologiesandarenoteffectiveforsuch sparsegraphs.Thus,thereis a great need to optimize these algorithms. Evolutionary algorithms have emerged as a promising solution for the optimized approximation of hard problems. The chapter“CommunityDetectionUsingNature-InspiredAlgorithms”proposesto optimizethecommunitydetectionmethodbasedonmodularityandNMIusingthe latest grey wolf optimization algorithm. The results demonstrate the effectiveness ofthealgorithms,comparedwithothercontemporaryevolutionaryalgorithms. One of the major tasks related to the structural social network analysis is the detectionandanalysisofcommunitystructures,whichishighlychallengingowing to consideration of various constraints when defining a community. For example, communitystructuresto be detectedmay bedisjoint, overlapping,or hierarchical, whereas community detection methods may vary depending on whether links are weightedor unweighted,directedor undirected,and whetherthe networkis static or dynamic. The chapter “A Unified Framework for Community Structure AnalysisinDynamicSocialNetworks”presentsaunifiedsocialnetworkanalysis frameworkthatismainlyaimedataddressingtheproblemofcommunityanalysis, including overlapping community detection, community evolution tracking and hierarchicalcommunitystructureidentificationinaunifiedmanner.Thechapteralso presents some important application areas wherein the knowledge of community structuresfacilitatesthegenerationofimportantanalyticalresultsandprocessesto helpsolveothersocialnetworkanalysis-relatedproblems. The chapter “GSO-Based Heuristics for Identification of Communities and Their Leaders” focuses on identifying leaders in communities detected in social networks. It presents a framework-based approach for extraction of communi- ties using a very efficient nature-based DTL-GSO heuristic optimization algo- rithm based on the group search optimization algorithm. Using the proposed approach, designated communities and their leaders facilitate the task of harmo- nizing community-specific assignments in a more efficient manner, leading to an overall performance boost. The significant contribution of this chapter is a novel metrictoidentifycommunity-specificcentralnodes(leaders),whicharealsohighly trustedgloballyintheoverallnetwork. x Preface Interesting applicationsof social networksincludeviral marketing,recommen- dation systems, poll analysis, etc. In these applications user influence plays an important role. The chapter “A Holistic Approach to Influence Maximization” discusseshoweffectivelysocialnetworkscanbeusedforinformationpropagation in the context of viral marketing. Picking the right group of users, hoping they will cause a chain effect of marketing, forms the core concept of viral marketing applications.Thestrategyusedtoselectthecorrectgroupofusersistheinfluence maximizationproblem.Thechapterproposesoneoftheviablesolutionstoinfluence maximization. The focus is to find those users in the social networks who would adoptandpropagateinformation,thusresultinginan effectivemarketingstrategy. Thethreemaincomponentsthatwouldhelpin theeffectivespreadofinformation inthesocialnetworksarethenetworkstructure,theuser’sinfluenceonothersand the seedingalgorithm.An amalgamationof these three aspectsprovidesa holistic solutiontoinfluencemaximization. Opinion dynamics, yet another aspect of social networks, has witnessed a colossal interest in research and development activity aimed at the realization of intelligentsystems,facilitatingandpredictingunderstanding.Manynature-inspired phenomenahave been used for modelling and investigationof opinionformation. Oneofthe prominentmodelsbasedontheconceptofferromagnetismisthe Ising model in statistical mechanics. This model represents magnetic dipole moments of atomic spins, which can exist in any one of two states, +1 or (cid:2)1. In the chapter “Opinion Dynamics Through Natural Phenomenon of Grain Growth and Population Migration” NetLogo is used to simulate the Ising model and correlates the results with opinion dynamics in its purview. The novelty of the chapter lies in the fact that the grain growth phenomenon has been investigated to analyze opinion dynamics, providing a new unexplored dimension of studying opiniondynamics.Thechapterpresentsamodellingofthenaturalphenomenonof population growth using rigorous mathematics and corroborated the results with opiniondynamics. Aclosecorrelationbetweenopiniondynamicsandsentimentanalysisformsthe basisofthechapter“OpinionMiningfromSocialTravelNetworks”.Thisdeals with the extraction of emotions expressed in natural language into digital form. It is being used in every field to monitor people’s emotions and sentiments. The chapter studies and analyzes the emotions expressed by people about their travel experiences. Online social networks promote faster propagationof new ideas and thoughts. Thepercolationofsocialmediaintoourdailylifehasinfluencedthewayinwhich consumersinteract and behave across the world. The traditional media marketing toolsarefacingincreasedcompetitionfromthemoreattractivealternativesprovided by social media. With more than two thirds of the internet population connected through online networking sites such as Facebook, Twitter and Myspace, the potentialofferedby thismediumis tremendous.Thechapter“FacilitatingBrand PromotionThroughOnlineSocialMedia:ABusinessCaseStudy”analysesthe case studies ofthree brandpromotionactivitiesthroughFacebookand modelsthe mechanicsofsuccessfuldiffusionincomparisonwithtraditionalchannels. Preface xi Anotherinterestingaspectofonlinee-commercesystemsaretheusers’reviews and ratings of products through their daily interaction. The chapter “Product Diffusion Pattern Analysis Model Based on Users’ Reviews of E-commerce Applications” studies product diffusion pattern analysis as an impact of users’ reviews on social e-commercegiant Amazon as a functionof time. The proposed userreview-basedproductdiffusionpatternanalysis(PDPA)modelextractsreviews and its associated properties, such as review ratings, comments on reviews and the helpfulnessof reviewsusing Amazon applicationprogramminginterfaces and assigning weight to all reviews according to the associated properties mentioned. The model predicts the long-term dynamics of the product on popular interactive e-commercesitesbyanalyzingtheusers’offeredreviewsandratingsonthesesites. The introduced users’ review feature-based rise and fall productdiffusion pattern analysismodeladdsasenseofqualityassuranceforproductsandservices,whichis anewdimensionoforganicmarketing. Assessing the quality of the product becomes very important. Product review classification is used to analyze the sentiment from reviews posted by the user to prepare the product report. The chapter “Hierarchical Sentiment Analysis Model for Automatic Review Classification for E-commerce Users” proposes a mechanismfor opinionminingovertext review data to generateproductreview reports based upon multiple features combined. This report shows positive and negative points about the specific product, which can play a significant role in the selection of the products on the online portals. The evolutionary history of the human brain shows advancement in its complexity and creativity during its evolutionary path from early primates to hominids and finally to Homo sapiens. Whenaproblemarises,humansmakeuseoftheirintelligenceandvariousmethods tofindthesolution.Nodoubt,theyhavecomeupwiththebestsolutions,butmany questions have been asked about how that problem is approached and how the solution is derived.The peculiar thing is that everyonehas a differentmechanism of thinking and comes up with differentpatterns of solutions. Can this pattern be mimicked by a machine where a problem can be solved by inputs from multiple individuals? The chapter “Trends and Pattern Analysis in Social Networks” addresses this issue using the concepts of crowd sourcing and neural networks. Crowd sourcing deals with the pooling of ideas by people. The more people, the wider theperspectiveweget.Thedatagivenbythemisprocessedandthefieldofneural networksplaysavitalroleinanalyzingthedata.Thesedatacontainvariouspatterns andhiddensolutionstomanyproblems. Using social networking in higher education is yet another critical domain of socialnetworksowingtothecomplexnatureofservingthepopulationoftheonline generation. Utilizing social networking environmentsfor learning and teaching at HEIscouldbeafinanciallyeffectiveandproductivewayofspeakingandconnecting with online higher education members. The chapter “Extensible Platform of CrowdsourcingonSocialNetworkingSites:AnAnalysis”contributesintermsof analysingthetechnologicalbehaviourpatterns(theiractiveorrelevantengagement)
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