Broad Learning Through Fusions

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.

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

Autorentext
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.

Inhalt
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.