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020 _a9789813340220
_9978-981-33-4022-0
024 7 _a10.1007/978-981-33-4022-0
_2doi
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_2bicssc
072 7 _aCOM004000
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082 0 4 _a006.3
_223
100 1 _aAggarwal, Manasvi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
300 _aXI, 112 p. 29 illus., 18 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 _aSpringerBriefs in Computational Intelligence,
_x2625-3712
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) .
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
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
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