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Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs - Couverture souple
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
M.N. Murty is currently a Professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore. His research interests are in the area of pattern recognition, data mining, and social network analysis.
Ms. Manasvi Aggarwal is currently pursuing her M.S. at the Indian Institute of Science, Bangalore. Her research interest is in the areas of social networks and machine learning
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Machine Learning in Social Networks
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Springer Singapore, 2020
ISBN 10 : 9813340215
ISBN 13 : 9789813340213
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Machine Learning in Social Networks
Edité par
Springer Nature Singapore Nov 2020, 2020
ISBN 10 : 9813340215
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9789813340213
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Machine Learning in Social Networks : Embedding Nodes, Edges, Communities, and Graphs
Edité par
Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10 : 9813340215
ISBN 13 : 9789813340213
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9789813340213
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Machine Learning in Social Networks
Edité par
Springer Nature Singapore, Springer Nature Singapore Nov 2020, 2020
ISBN 10 : 9813340215
ISBN 13 : 9789813340213
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 124 pp. Englisch. N° de réf. du vendeur 9789813340213
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