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001432695 0247_ $$a10.1007/978-981-33-4022-0$$2doi
001432695 035__ $$aSP(OCoLC)1224513586
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001432695 08204 $$a006.3/1$$223
001432695 1001_ $$aAggarwal, Manasvi,$$eauthor.
001432695 24510 $$aMachine learning in social networks :$$bembedding nodes, edges, communities, and graphs /$$cManasvi Aggarwal, M.N. Murty.
001432695 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001432695 300__ $$a1 online resource (xi, 112 pages) :$$billustrations (color, black and white)
001432695 336__ $$atext$$btxt$$2rdacontent
001432695 337__ $$acomputer$$bc$$2rdamedia
001432695 338__ $$aonline resource$$bcr$$2rdacarrier
001432695 347__ $$atext file
001432695 347__ $$bPDF
001432695 4901_ $$aSpringerBriefs in applied sciences and technology. Computational intelligence,$$x2625-3704
001432695 504__ $$aIncludes bibliographical references and index.
001432695 5050_ $$aIntroduction -- Representations of Networks -- Deep Learning -- Node Representations -- Embedding Graphs -- Conclusions.
001432695 506__ $$aAccess limited to authorized users.
001432695 520__ $$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 proteinprotein 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 of current 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.
001432695 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 2, 2021).
001432695 650_0 $$aMachine learning.
001432695 650_0 $$aComputational intelligence.
001432695 650_0 $$aArtificial intelligence.
001432695 650_0 $$aNeural networks (Computer science)
001432695 650_6 $$aApprentissage automatique.
001432695 650_6 $$aIntelligence informatique.
001432695 650_6 $$aIntelligence artificielle.
001432695 650_6 $$aRéseaux neuronaux (Informatique)
001432695 655_0 $$aElectronic books.
001432695 7001_ $$aMurty, M. Narasimha,$$eauthor.
001432695 77608 $$iPrint version:$$aAggarwal, Manasvi.$$tMachine learning in social networks.$$dSingapore : Springer, [2021]$$z9813340215$$z9789813340213$$w(OCoLC)1198974359
001432695 830_0 $$aSpringerBriefs in applied sciences and technology.$$pComputational intelligence.$$x2625-3704
001432695 852__ $$bebk
001432695 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-33-4022-0$$zOnline Access$$91397441.1
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001432695 980__ $$aBIB
001432695 980__ $$aEBOOK
001432695 982__ $$aEbook
001432695 983__ $$aOnline
001432695 994__ $$a92$$bISE