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Broad Learning Through Fusions

An Application on Social Networks

Authors: Zhang, Jiawei, Yu, Philip S.

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  • Provides an introduction to broad learning, focusing on the fundamental concepts, learning tasks, and methodologies to build learning models for data fusion, and knowledge discovery
  • Examines how the introduced broad learning approaches can be applied for effective data fusion and knowledge discovery on online social networks
  • Introduces the social network alignment task and learning algorithms based on three different learning settings
  • Provides a comprehensive introduction to the several well-known knowledge discovery problems with the fused information from multiple online social networks
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eBook 83,29 €
price for Spain (gross)
  • ISBN 978-3-030-12528-8
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 103,99 €
price for Spain (gross)
  • ISBN 978-3-030-12527-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this Textbook

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

About the authors

Jiawei Zhang is Assistant Professor in the Department of Computer Science at Florida State University. In 2017 he founded IFM Lab, a research oriented academic laboratory, providing the latest information on fusion learning and data mining research works and application tools to both academia and industry.
Philip S. Yu is Professor in the Department of Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information and Technology. He was manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. Dr. Yu has published more than 500 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents. 


Table of contents (12 chapters)

Table of contents (12 chapters)

Buy this book

eBook 83,29 €
price for Spain (gross)
  • ISBN 978-3-030-12528-8
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 103,99 €
price for Spain (gross)
  • ISBN 978-3-030-12527-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Broad Learning Through Fusions
Book Subtitle
An Application on Social Networks
Authors
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-12528-8
DOI
10.1007/978-3-030-12528-8
Hardcover ISBN
978-3-030-12527-1
Edition Number
1
Number of Pages
XV, 419
Number of Illustrations
23 b/w illustrations, 81 illustrations in colour
Topics