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020 _a9783030112868
_9978-3-030-11286-8
024 7 _a10.1007/978-3-030-11286-8
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
245 1 0 _aFrom Security to Community Detection in Social Networking Platforms
_h[electronic resource] /
_cedited by Panagiotis Karampelas, Jalal Kawash, Tansel Özyer.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aX, 237 p. 98 illus., 70 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Social Networks,
_x2190-5436
505 0 _aChapter1. Real-world application of ego-network analysis to evaluate environmental management structures -- Chapter2. An Evolutionary Approach for Detecting Communities in Social Networks -- Chapter3. On Detecting Multidimensional Communities -- Chapter4. Derivatives in Graph Space with Applications for Finding and Tracking Local Communities -- Chapter5. Graph Clustering Based on Attribute-aware Graph Embedding -- Chapter6. On Counting Triangles through Edge Sampling in Large Dynamic Graphs -- Chapter7. Generation and Corruption of Semi-structured and Structured Data -- Chapter8. A Data Science Approach to Predict the Impact of Collateralization on Systemic Risk -- Chapter9. Mining actionable information from security forums: the case of malicious IP addresses -- Chapter10. Temporal Methods to Detect Content-Based Anomalies in Social Media.
520 _aThis 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.
650 0 _aData mining.
650 0 _aSociology
_xMethodology.
650 0 _aQuantitative research.
650 0 _aSocial sciences
_xData processing.
650 0 _aSystem theory.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aSociological Methods.
650 2 4 _aData Analysis and Big Data.
650 2 4 _aComputer Application in Social and Behavioral Sciences.
650 2 4 _aComplex Systems.
700 1 _aKarampelas, Panagiotis.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKawash, Jalal.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aÖzyer, Tansel.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030112851
776 0 8 _iPrinted edition:
_z9783030112875
830 0 _aLecture Notes in Social Networks,
_x2190-5436
856 4 0 _uhttps://doi.org/10.1007/978-3-030-11286-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c174673
_d174673