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Titlebook: Machine Learning in Social Networks; Embedding Nodes, Edg Manasvi Aggarwal,M.N. Murty Book 2021 The Author(s), under exclusive license to S

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发表于 2025-3-21 19:22:55 | 显示全部楼层 |阅读模式
书目名称Machine Learning in Social Networks
副标题Embedding Nodes, Edg
编辑Manasvi Aggarwal,M.N. Murty
视频video
概述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
丛书名称SpringerBriefs in Applied Sciences and Technology
图书封面Titlebook: Machine Learning in Social Networks; Embedding Nodes, Edg Manasvi Aggarwal,M.N. Murty Book 2021 The Author(s), under exclusive license to S
描述.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
出版日期Book 2021
关键词Network embedding; Deep Learning (DL); Neural Networks; Network representation learning; Embedded graphs
版次1
doihttps://doi.org/10.1007/978-981-33-4022-0
isbn_softcover978-981-33-4021-3
isbn_ebook978-981-33-4022-0Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
copyrightThe Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
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发表于 2025-3-21 20:18:50 | 显示全部楼层
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https://doi.org/10.1007/978-981-33-4022-0Network embedding; Deep Learning (DL); Neural Networks; Network representation learning; Embedded graphs
发表于 2025-3-22 05:02:35 | 显示全部楼层
Embedding Graphs,There are several applications where an embedding or a low-dimensional representation of the entire graph is required. This chapter deals with such representations which are called .. We consider various state-of-the-art graph pooling techniques that are important in this context. We also consider . tasks including ., and
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Conclusions,this book we have examined ., and their analysis. Specifically, we considered the following aspects.
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978-981-33-4021-3The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
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Machine Learning in Social Networks978-981-33-4022-0Series ISSN 2191-530X Series E-ISSN 2191-5318
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