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Titlebook: Hybrid Intelligence for Social Networks; Hema Banati,Siddhartha Bhattacharyya,Mario K?ppen Book 2017 Springer International Publishing AG

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發(fā)表于 2025-3-21 19:17:29 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Hybrid Intelligence for Social Networks
編輯Hema Banati,Siddhartha Bhattacharyya,Mario K?ppen
視頻videohttp://file.papertrans.cn/431/430096/430096.mp4
概述Contributions provide a multidimensional approach.Useful reference for graduate students and researchers in computer science, electronics engineering, communications, and information technology.Explai
圖書封面Titlebook: Hybrid Intelligence for Social Networks;  Hema Banati,Siddhartha Bhattacharyya,Mario K?ppen Book 2017 Springer International Publishing AG
描述.This book explains aspects of social networks, varying from development and application of new artificial intelligence and computational intelligence techniques for social networks to understanding the impact of social networks. Chapters 1 and 2 deal with the basic strategies towards social networks such as mining text from such networks and applying social network metrics using a hybrid approach; Chaps. 3 to 8 focus on the prime research areas in social networks: community detection, influence maximization and opinion mining. Chapter 9 to 13 concentrate on studying the impact and use of social networks in society, primarily in education, commerce, and crowd sourcing.?.The contributions provide a multidimensional approach, and the book will serve graduate students and researchers as a reference in computer science, electronics engineering, communications, and information technology.?.
出版日期Book 2017
關(guān)鍵詞Social networks; Soft computing; Data mining; Nature-inspired computing; Community detection; Complex net
版次1
doihttps://doi.org/10.1007/978-3-319-65139-2
isbn_softcover978-3-319-87955-0
isbn_ebook978-3-319-65139-2
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Community Detection Using Nature Inspired Algorithm,as of importance, whether social network, citation network, traffic network, metabolic network, protein-protein network or web graph etc. Mining meaningful communities in a real-world network is a hard problem owing to the dynamic nature of these networks. The existing algorithms for community detec
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A Unified Framework for Community Structure Analysis in Dynamic Social Networks,g owing to consideration of various constraints while defining a community. For example, community structures to be detected may be disjointed, overlapping, or hierarchical, whereas on the other hand, community detection methods may vary depending on whether links are weighted or unweighted, directe
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A Holistic Approach to Influence Maximization,everyone connected. Moreover, the popularity of web and social networks has interesting applications including viral marketing, recommendation systems, poll analysis, etc. In these applications, user influence plays an important role. This chapter discusses how effectively social networks can be use
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