Broad Learning Through Fusions
An Application on Social Networks
Authors: Zhang, Jiawei, Yu, Philip S.
Free Preview- This book 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. It examines how the introduced broad learning approaches can be applied for effective data fusion and knowledge discovery on online social networks. The book Introduces the social network alignment task and learning algorithms based on three different learning settings. It provides a comprehensive introduction to the several well-known knowledge discovery problems with the fused information from multiple online social networks
Buy this book
- 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)
-
-
Broad Learning Introduction
Pages 3-17
-
Machine Learning Overview
Pages 19-75
-
Social Network Overview
Pages 77-126
-
Supervised Network Alignment
Pages 129-164
-
Unsupervised Network Alignment
Pages 165-202
-
Table of contents (12 chapters)
Buy this book
Services for this Book
Recommended for you
Bibliographic Information
- Bibliographic Information
-
- Book Title
- Broad Learning Through Fusions
- Book Subtitle
- An Application on Social Networks
- Authors
-
- Jiawei Zhang
- Philip S. Yu
- 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
- Softcover ISBN
- 978-3-030-12530-1
- Edition Number
- 1
- Number of Pages
- XV, 419
- Number of Illustrations
- 23 b/w illustrations, 81 illustrations in colour
- Topics
*immediately available upon purchase as print book shipments may be delayed due to the COVID-19 crisis. ebook access is temporary and does not include ownership of the ebook. Only valid for books with an ebook version. Springer Reference Works are not included.
