000 04253nam a22005655i 4500
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
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM075000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _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.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
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.
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
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cLIBRO_ELEC
999 _c242897
_d242896