Broad Learning Through Fusions An Application on Social Networks

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 ta...

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Bibliographic Details
Main Authors: Zhang, Jiawei, Yu, Philip S. (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2019, 2019
Edition:1st ed. 2019
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Description
Summary: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
Physical Description:XV, 419 p. 104 illus., 81 illus. in color online resource
ISBN:9783030125288