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Broad Learning Through Fusions [electronic resource] :An Application on Social Networks /

By: Zhang, Jiawei [author.].
Contributor(s): Yu, Philip S [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2019Description: XV, 419 p. 104 illus., 81 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030125288.Subject(s): Computer Science | Data mining | Artificial intelligence | Data structures (Computer scienc | Computer science | Data Mining and Knowledge Discovery | Artificial Intelligence | Data Structures | Information Systems Applications (incl. Internet) | Probability and Statistics in Computer ScienceOnline resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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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.

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.

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