Jia-wei Zhang, Philip S Yu

Broad Learning Through Fusions

An Application on Social Networks

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-12530-1
• 1st ed. 2019
• Seitenzahl: 419
• Englisch
• Abmessung: 25mm x 180mm x 255mm
• ISBN-13: 9783030125301
• Artikelnr.: 59796263

Autorenporträt

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.

Inhaltsangabe

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.