000 | 04253nam a22005655i 4500 | ||
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001 | 978-3-319-96746-2 | ||
003 | DE-He213 | ||
005 | 20210201191403.0 | ||
007 | cr nn 008mamaa | ||
008 | 180825s2018 gw | s |||| 0|eng d | ||
020 |
_a9783319967462 _9978-3-319-96746-2 |
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_a004.6 _223 |
100 | 1 |
_aRaj P.M., Krishna. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
245 | 1 | 0 |
_aPractical Social Network Analysis with Python _h[electronic resource] / _cby Krishna Raj P.M., Ankith Mohan, K.G. Srinivasa. |
250 | _a1st ed. 2018. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018. |
|
300 |
_aXXXI, 329 p. 186 illus., 73 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 |
||
490 | 1 |
_aComputer Communications and Networks, _x1617-7975 |
|
500 | _aAcceso multiusuario | ||
505 | 0 | _aChapter 1. Basics of Graph Theory -- Chapter 2. Graph Structure of the Web -- Chapter 3. Random Graph Models -- Chapter 4. Small World Phenomena -- Chapter 5. Graph Structure of Facebook -- Chapter 6. Peer-To-Peer Networks -- Chapter 7. Signed Networks -- Chapter 8. Cascading in Social Networks -- Chapter 9. Influence Maximisation -- Chapter 10. Outbreak Detection -- Chapter 11. Power Law -- Chapter 12. Kronecker Graphs -- Chapter 13. Link Analysis -- Chapter 14. Community Detection -- Chapter 15. Representation Learning on Graph. | |
520 | _aThis book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network. It covers different forms of graphs and their analysis using techniques like filtering, clustering and rule mining, as well as important theories like small world phenomenon. It also presents methods for identifying influential nodes in the network and information dissemination models. Further, it uses examples to explain the tools for visualising large-scale networks, and explores emerging topics like big data and deep learning in the context of social network analysis. With the Internet becoming part of our everyday lives, social networking tools are used as the primary means of communication. And as the volume and speed of such data is increasing rapidly, there is a need to apply computational techniques to interpret and understand it. Moreover, relationships in molecular structures, co-authors in scientific journals, and developers in a software community can also be understood better by visualising them as networks. This book brings together the theory and practice of social network analysis and includes mathematical concepts, computational techniques and examples from the real world to offer readers an overview of this domain. | ||
541 |
_fUABC ; _cTemporal ; _d01/01/2021-12/31/2023. |
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650 | 0 | _aComputer communication systems. | |
650 | 0 | _aPython (Computer program language). | |
650 | 1 | 4 |
_aComputer Communication Networks. _0https://scigraph.springernature.com/ontologies/product-market-codes/I13022 |
650 | 2 | 4 |
_aPython. _0https://scigraph.springernature.com/ontologies/product-market-codes/I29080 |
700 | 1 |
_aMohan, Ankith. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
700 | 1 |
_aSrinivasa, K.G. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319967455 |
776 | 0 | 8 |
_iPrinted edition: _z9783319967479 |
776 | 0 | 8 |
_iPrinted edition: _z9783030072414 |
830 | 0 |
_aComputer Communications and Networks, _x1617-7975 |
|
856 | 4 | 0 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-96746-2 |
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