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dc.contributor.editorKarampelas, Panagiotis
dc.contributor.editor Kawash, Jalal
dc.contributor.editor Ozyer, Tansel
dc.date.accessioned2020-01-28T12:18:09Z
dc.date.available2020-01-28T12:18:09Z
dc.date.issued2019-01
dc.identifier.citationKarampelas, P., Kawash, J., and Özyer, T. (Eds.). (2019). From Security to Community Detection in Social Networking Platforms. Springer International Publishing.en_US
dc.identifier.isbn978-3-030-11285-1
dc.identifier.isbn978-3-030-11286-8
dc.identifier.urihttps://www.springer.com/gp/book/9783030112851
dc.identifier.urihttp://hdl.handle.net/20.500.11851/3309
dc.descriptionThis book focuses on novel and state-of-the-art scientific work in the area of detection and prediction techniques using information found generally in graphs and particularly in social networks. Community detection techniques are presented in diverse contexts and for different applications while prediction methods for structured and unstructured data are applied to a variety of fields such as financial systems, security forums, and social networks. The rest of the book focuses on graph-based techniques for data analysis such as graph clustering and edge sampling. The research presented in this volume was selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. This book will appeal to practitioners, researchers and students in the field.en_US
dc.description.tableofcontents[Pages 1-16] Real-World Application of Ego-Network Analysis to Evaluate Environmental Management Structures [Pages 17-44] An Evolutionary Approach for Detecting Communities in Social Networks [Pages 45-78] On Detecting Multidimensional Communities [Pages 79-107] Derivatives in Graph Space with Applications for Finding and Tracking Local Communities [Pages 109-131] Graph Clustering Based on Attribute-Aware Graph Embedding [Pages 133-157] On Counting Triangles Through Edge Sampling in Large Dynamic Graphs [Pages 159-169] Generation and Corruption of Semi-Structured and Structured Data [Pages 171-192] A Data Science Approach to Predict the Impact of Collateralization on Systemic Risk [Pages 193-211] Mining Actionable Information from Security Forums: The Case of Malicious IP Addresses [Pages 213-230] Temporal Methods to Detect Content-Based Anomalies in Social Mediaen_US
dc.language.isoengen_US
dc.publisherSpringer International Publishingen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectComputer scienceen_US
dc.subjectinformation systemsen_US
dc.subjectcommunicationen_US
dc.titleFrom Security to Community Detection in Social Networking Platformsen_US
dc.typebooken_US
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Computer Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage1
dc.identifier.endpage230
dc.contributor.tobbetuauthorÖzyer, Tansel
dc.contributor.YOKid143116
dc.identifier.doi10.1007/978-3-030-11286-8
dc.relation.publicationcategoryKitap - Uluslararasıtr_TR


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