Machine Learning in Social Networks

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Beschreibung

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.

Über den Autor

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

Zusammenfassung

Highlights the understanding of complex systems in different domains including health, education, agriculture, and transportation

Combines both conventional machine learning (ML) and deep learning (DL) techniques to understand complex systems

Presents neural networks and Deep Learning (DL) techniques useful in network embedding

Inhaltsverzeichnis

Introduction.- Representations of Networks.- Deep Learning.- Node Representations.- Embedding Graphs .- Conclusions.

Details

Erscheinungsjahr:	2020
Fachbereich:	Technik allgemein
Genre:	Importe , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xi 112 S. 11 s/w Illustr. 18 farbige Illustr. 112 p. 29 illus. 18 illus. in color.
ISBN-13:	9789813340213
ISBN-10:	9813340215
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Murty, M. N. Aggarwal, Manasvi
Auflage:	1st edition 2021
Hersteller:	Springer Singapore Springer Nature Singapore
Verantwortliche Person für die EU:	Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße:	235 x 155 x 8 mm
Von/Mit:	M. N. Murty (u. a.)
Erscheinungsdatum:	26.11.2020
Gewicht:	0,201 kg

Artikel-ID: 119024446