000 | 03585nam a22005655i 4500 | ||
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001 | 978-981-33-4022-0 | ||
003 | DE-He213 | ||
005 | 20240423125319.0 | ||
007 | cr nn 008mamaa | ||
008 | 201125s2021 si | s |||| 0|eng d | ||
020 |
_a9789813340220 _9978-981-33-4022-0 |
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024 | 7 |
_a10.1007/978-981-33-4022-0 _2doi |
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050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
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_aCOM004000 _2bisacsh |
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_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aAggarwal, Manasvi. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aMachine Learning in Social Networks _h[electronic resource] : _bEmbedding Nodes, Edges, Communities, and Graphs / _cby Manasvi Aggarwal, M.N. Murty. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2021. |
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300 |
_aXI, 112 p. 29 illus., 18 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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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Computational Intelligence, _x2625-3712 |
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505 | 0 | _aIntroduction -- Representations of Networks -- Deep Learning -- Node Representations -- Embedding Graphs -- Conclusions. | |
520 | _aThis book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein–protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area ofcurrent interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties. . | ||
650 | 0 | _aComputational intelligence. | |
650 | 0 | _aMachine learning. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aNeural networks (Computer science)Â . | |
650 | 1 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aMathematical Models of Cognitive Processes and Neural Networks. |
700 | 1 |
_aMurty, M.N. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789813340213 |
776 | 0 | 8 |
_iPrinted edition: _z9789813340237 |
830 | 0 |
_aSpringerBriefs in Computational Intelligence, _x2625-3712 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-33-4022-0 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c176599 _d176599 |