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Machine Learning in Social Networks: Embedding Nodes,Edges,Communities,and Graphs (Briefs in Applied Sciences and Technology)
by 作者: Manasvi Aggarwal and M.N. Murty
pages 页数: 124 pages
ISBN-10 书号: 9813340215
ISBN-13 书号: 9789813340213
Dimensions: 6.1 x 0.3 x 9.25 inches
Item Weight: 6.6 ounces
Publisher Finelybook 出版社: ; 1st ed. 2021 edition (November 26,2020)
Language 语言: English
Book Description
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 作者: 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 作者: leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by 作者: 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.
