Broad Learning Through Fusions
An Application on Social Networks
Published on 14. August 2020
Book
Paperback/Softback
XV, 419 pages
978-3-030-12530-1 (ISBN)
Description
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.
More details
Edition
2019 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Primary & secondary/elementary & high school
Illustrations
81 farbige Abbildungen, 23 s/w Abbildungen
XV, 419 p. 104 illus., 81 illus. in color.
Dimensions
Height: 25.4 cm
Width: 17.8 cm
Weight
824 gr
ISBN-13
978-3-030-12530-1 (9783030125301)
DOI
10.1007/978-3-030-12528-8
Schweitzer Classification
Other editions
Additional editions
Book
06/2019
Springer
€53.49
Shipment within 7-9 days
Persons
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.
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.
Content
1 Broad Learning Introduction.- 2 Machine Learning Overview.- 3 Social Network Overview.- 4 Supervised Network Alignment.- 5 Unsupervised Network Alignment.- 6 Semi-supervised Network Alignment.- 7 Link Prediction.- 8 Community Detection.- 9 Information Diffusion.- 10 Viral Marketing.- 11 Network Embedding.- 12 Frontier and Future Directions.- References.