000 | 04163nam a22006255i 4500 | ||
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001 | 978-3-030-11286-8 | ||
003 | DE-He213 | ||
005 | 20240423125134.0 | ||
007 | cr nn 008mamaa | ||
008 | 190409s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030112868 _9978-3-030-11286-8 |
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024 | 7 |
_a10.1007/978-3-030-11286-8 _2doi |
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050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
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_aUYQE _2thema |
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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. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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. |
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650 | 0 | _aQuantitative research. | |
650 | 0 |
_aSocial sciences _xData processing. |
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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 |
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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 |