9789813340213 - Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs di Aggarwal, Manasvi; Murty, M.N. (15 risultati)

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Condizione: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book deals withnetworkrepresentation 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 bymodeling, 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 andprotein-proteininteraction 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)andcommunity 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 alow-/high-dimensionvector space maintaining all the relevant properties.

Taschenbuch. Condizione: Neu. Machine Learning in Social Networks | Embedding Nodes, Edges, Communities, and Graphs | Manasvi Aggarwal (u. a.) | Taschenbuch | SpringerBriefs in Applied Sciences and Technology | xi | Englisch | 2020 | Springer | EAN 9789813340213 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book deals withnetworkrepresentation 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 bymodeling, 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 andprotein-proteininteraction 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)andcommunity 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 alow-/high-dimensionvector space maintaining all the relevant properties. 124 pp. Englisch.

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Highlights the understanding of complex systems in different domains including health, education, agriculture,&nbspand transportationCombines both&nbspconventional machine learning (ML) and deep learning (DL) techniques to understand com.

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 124 pp. Englisch.