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294 Pages·2015·11.148 MB·English
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Lecture Notes in Social Networks Özgür Ulusoy Abdullah Uz Tansel Erol Arkun Editors Recommendation and Search in Social Networks Lecture Notes in Social Networks Series editors Reda Alhajj, University of Calgary, Calgary, AB, Canada Uwe Glässer, Simon Fraser University, Burnaby, BC, Canada Advisory Board Charu Aggarwal, IBM T.J. Watson Research Center, Hawthorne, NY, USA Patricia L. Brantingham, Simon Fraser University, Burnaby, BC, Canada Thilo Gross, University of Bristol, UK Jiawei Han, University of Illinois at Urbana-Champaign, IL, USA Huan Liu, Arizona State University, Tempe, AZ, USA Raúl Manásevich, University of Chile, Santiago, Chile Anthony J. Masys, Centre for Security Science, Ottawa, ON, Canada Carlo Morselli, University of Montreal, QC, Canada Rafael Wittek, University of Groningen, The Netherlands Daniel Zeng, The University of Arizona, Tucson, AZ, USA More information about this series at http://www.springer.com/series/8768 Ö ü zg r Ulusoy Abdullah Uz Tansel (cid:129) Erol Arkun Editors Recommendation and Search in Social Networks 123 Editors ÖzgürUlusoy ErolArkun Department of Computer Engineering Department of Computer Engineering Bilkent University Bilkent University Ankara Ankara Turkey Turkey AbdullahUz Tansel Department of Statistics andComputer InformationSystems Baruch College,CUNY New York, NY USA ISSN 2190-5428 ISSN 2190-5436 (electronic) Lecture Notesin SocialNetworks ISBN 978-3-319-14378-1 ISBN 978-3-319-14379-8 (eBook) DOI 10.1007/978-3-319-14379-8 LibraryofCongressControlNumber:2014959199 SpringerChamHeidelbergNewYorkDordrechtLondon ©SpringerInternationalPublishingSwitzerland2015 Chapter4wascreatedwithinthecapacityofanUSgovernmentalemployment.UScopyrightprotection doesnotapply. Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexempt fromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthis book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained hereinorforanyerrorsoromissionsthatmayhavebeenmade. Printedonacid-freepaper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com) Preface This book is a timely collection of 12 chapters that present the state of the art in various aspects of social search and recommendation systems. Within the broader contextofsocialnetwork analysis, itfocusesonimportantandupcomingtopics of socialsearchandrecommendationsystems.Webelievethatthebookisacoherent collection of chapters which is not easily accomplished in edited volumes. Many of the chapters are expanded versions of the best papers presented in the IEEE/ACM International Conference on Advances in Social Networks Analysis andMining(ASONAM’2013),whichwasheldinNiagaraFalls,CanadainAugust 2013. The papers were selected based on the reviews for the conference and then were improved substantially by the authors. In addition to the selected papers, the bookalsofeaturesinvitedchaptersinthefieldofsocialsearchandrecommendation systems. The first chapter, by Xinyue Wang, Laurissa Tokarchuk, Felix Cuadrado and Stefan Poslad, presents an adaptive crawling model to detect emerging popular topics,bysearchingforhighlycorrelateddatafortheeventsofinterest.Inthenext chapter, Yuki Urabe, Rafal Rzepka, and Kenji Araki propose an emoticon rec- ommendation system based on users’ emotional statements and evaluate its per- formance in comparison to other such recommendation systems. Then, Georgios Alexandridis, Giorgos Siolas, and Andreas Stafylopatis present a novel random walk social recommendation approach based on rejection sampling. In “Social Network Derived Credibility,” Erica Briscoe, Darren Appling and Heather Hayes explore the use of social network properties as a basis for deter- mining credibility. Inthenextchapter, Benjamin C.M.Fung, Yan’An Jin, Jiaming Li, and Junqiang Liu propose a method to anonymize the social network with the goalsofhidingtheidentitiesoftheparticipantsandpreservingthefrequentsharing patterns within a community. In the following chapter, Cheng Chen, Kui Wu, Venkatesh Srinivasan, and Xudong Zhang present a new detection mechanism, usingbothsemanticandnonsemanticanalysis,toidentifyaspecialgroupofonline users, called hidden paid posters. In their work, Sogol Naseri, Arash Bahrehmand, and Chen Ding strive to enhance recommendation accuracy through the use of a new similarity metric v vi Preface whichisbasedonsocialtagginginformation.Theyalsopresentarecommendation method that applies user similarity for finding the most interesting items to target user’staste.AliKhodaei,CyrusShahabi,andSinaSohangirproposeanewmodel, calledpersocialrelevancemodelutilizingsocialsignalstoimprovethewebsearch, in their chapter titled “Personalization of Web Search Using Social Signals”. Linhong Zhu, Sheng Gao, Sinno Jialin Pan, Haizhou Li, Dingxiong Deng, and CyrusShahabiprovideaformulationfortheinformativesentenceselectionproblem in opinion summarization as a community leader detection problem. Then, they present new algorithms to identify communities and leaders. ThechapterbyHasanShahidFerdous,MashruraTasnim,SaifAhmed,andMd. TanvirAlamAnikexploresdifferencesinsearchinghabitsofthesocialnetworking sitesindifferentregionsoftheworldbasedontheirlevelofeconomicdevelopment. In“EvolutionaryInfluenceMaximizationinViralMarketing”,SanketNaikandQi Yu propose a new framework to effectively apply viral marketing in a dynamic socialnetwork.ThelastchapterbyAlessiaAmeliopresentsaninvestigationofthe votingbehavioroftheItalianParliamentbyemployingmethodsindataminingand network analysis fields. We would like to express our appreciation to all contributing authors; without their cooperation this book would not have been possible. Our special thanks are duetoMs.PaulineLichtveldforhersupportandtoDr.TanselÖzyerwhodiligently supported and coordinated the entire process of preparing this timely volume on social network analysis. Özgür Ulusoy Abdullah Uz Tansel Erol Arkun Contents Adaptive Identification of Hashtags for Real-Time Event Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Xinyue Wang, Laurissa Tokarchuk, Felix Cuadrado and Stefan Poslad Comparison of Emoticon Recommendation Methods to Improve Computer-Mediated Communication . . . . . . . . . . . . . . . . . . . . . . . . . 23 Yuki Urabe, Rafal Rzepka and Kenji Araki Accuracy Versus Novelty and Diversity in Recommender Systems: A Nonuniform Random Walk Approach . . . . . . . . . . . . . . . . . . . . . . 41 Georgios Alexandridis, Georgios Siolas and Andreas Stafylopatis Social Network Derived Credibility . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Erica J. Briscoe, Darren Scott Appling and Heather Hayes Anonymizing Social Network Data for Maximal Frequent-Sharing Pattern Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Benjamin C.M. Fung, Yan’an Jin, Jiaming Li and Junqiang Liu A Comprehensive Analysis of Detection of Online Paid Posters . . . . . . 101 Cheng Chen, Kui Wu, Venkatesh Srinivasan and Xudong Zhang An Improved Collaborative Recommendation System by Integration of Social Tagging Data . . . . . . . . . . . . . . . . . . . . . . . . 119 Sogol Naseri, Arash Bahrehmand and Chen Ding Personalization of Web Search Using Social Signals . . . . . . . . . . . . . . 139 Ali Khodaei, Sina Sohangir and Cyrus Shahabi vii viii Contents The Pareto Principle Is Everywhere: Finding Informative Sentences for Opinion Summarization Through Leader Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Linhong Zhu, Sheng Gao, Sinno Jialin Pan, Haizhou Li, Dingxiong Deng and Cyrus Shahabi Social Media Question Asking: A Developing Country Perspective. . . . 189 Hasan Shahid Ferdous, Mashrura Tasnim, Saif Ahmed and Md. Tanvir Alam Anik Evolutionary Influence Maximization in Viral Marketing . . . . . . . . . . 217 Sanket Anil Naik and Qi Yu Mining and Analyzing the Italian Parliament: Party Structure and Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Alessia Amelio and Clara Pizzuti Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Contributors Saif Ahmed BUET, Dhaka, Bangladesh Georgios Alexandridis School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Athens, Greece Alessia Amelio Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), Rende, CS, Italy Md. Tanvir Alam Anik BUET, Dhaka, Bangladesh Darren Scott Appling Georgia Tech, Atlanta, GA, USA Kenji Araki Hokkaido University, Graduate School of Information Science and Technology, Sapporo, Japan Arash Bahrehmand Department of Information and Communications Technol- ogies, Universitat Pompeu Fabra Barcelona, Barcelona, Spain Erica J. Briscoe Georgia Tech, Atlanta, GA, USA Cheng Chen University of Victoria, Victoria, Canada Felix Cuadrado School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK Dingxiong Deng University of Southern California, Los Angeles, USA Chen Ding Department of Computer Science, Ryerson University, Toronto, Canada Hasan Shahid Ferdous University of Melbourne, Parkville, VIC, Australia Benjamin C.M. Fung McGill University, Montreal, QC, Canada Sheng Gao Institute for Infocomm Research, Singapore, Singapore ix

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