This book presents recent developments on the theoretical, algorithmic, and application aspects of Big Data in Complex and Social Networks. The book consists of four parts, covering a wide range of topics.
The first part of the book focuses on data storage and data processing. It explores how the efficient storage of data can fundamentally support intensive data access and queries, which enables sophisticated analysis. It also looks at how data processing and visualization help to communicate information clearly and efficiently. The second part of the book is devoted to the extraction of essential information and the prediction of web content. The book shows how Big Data analysis can be used to understand the interests, location, and search history of users and provide more accurate predictions of User Behavior.
The latter two parts of the book cover the protection of privacy and security, and emergent applications of big data and social networks. It analyzes how to model rumor diffusion, identify misinformation from massive data, and design intervention strategies. Applications of big data and social networks in multilayer networks and multiparty systems are also covered in-depth.
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Section I Social Networks and Complex Networks |
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Chapter 1 Hyperbolic Big Data Analytics within Complex and Social Networks |
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3 | (34) |
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Chapter 2 Scalable Query and Analysis for Social Networks |
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37 | (28) |
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Section II Big Data and Web Intelligence |
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Chapter 3 Predicting Content Popularity in Social Networks |
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65 | (30) |
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Chapter 4 Mining User Behaviors in Large Social Networks |
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95 | (30) |
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Section III Security and Privacy Issues of Social Networks |
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Chapter 5 Mining Misinformation in Social Media |
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125 | (28) |
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Chapter 6 Rumor Spreading and Detection in Online Social Networks |
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153 | (30) |
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Chapter 7 A Survey on Multilayer Networks and the Applications |
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183 | (30) |
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Chapter 8 Exploring Legislative Networks in a Multiparty System |
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213 | (20) |
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| Index |
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233 | |
Dr. My T. Thai is a UF Research Foundation Professor and Associate Chair for Research in the Department of Computer and Information Sciences and Engineering at the University of Florida. She received her PhD degree in Computer Science from the University of Minnesota in 2005. Her current research interests include algorithms, cybersecurity, and optimization on network science and engineering, including communication networks, smart grids, social networks, and their interdependency. The results of her work have led to 5 books and 120+ articles published in various prestigious journals and conferences on networking and combinatorics.
Dr. Weili Wu is a full professor in the Department of Computer Science, University of Texas at Dallas. She received her Ph.D. in 2002 and M.S. in 1998 from Department of Computer Science, University of Minnesota, Twin City. Her current research mainly deals with the general research area of data communication and data management. Her research focuses on the design and analysis of algorithms for optimization problems that occur in wireless networking environments and various database systems. She has published more than 200 research papers in various prestigious journals and conferences.
Dr. Hui Xiong is currently a Full Professor of Management Science and Information Systems at Rutgers Business School and the Director of Rutgers Center for Information Assurance at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), and the ICDM-2011 Best Research Paper Award (2011). Dr. Xiong is a prominent researcher in the areas of business intelligence, data mining, big data, and geographic information systems (GIS). For his outstanding contributions to these areas, he was elected an ACM Distinguished Scientist. He has a distinguished academic record that includes 200+ referred papers.
