Authors:
- Includes recent and fundamental models and optimization algorithms on social computing
- Presents detailed techniques, models, problems, algorithms and experiments
- Contains practical applications in security, business, computing and engineering
Part of the book series: SpringerBriefs in Optimization (BRIEFSOPTI)
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Table of contents (5 chapters)
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Front Matter
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Back Matter
About this book
This self-contained book describes social influence from a computational point of view, with a focus on recent and practical applications, models, algorithms and open topics for future research. Researchers, scholars, postgraduates and developers interested in research on social networking and the social influence related issues will find this book useful and motivating. The latest research on social computing is presented along with and illustrations on how to understand and manipulate social influence for knowledge discovery by applying various data mining techniques in real world scenarios. Experimental reports, survey papers, models and algorithms with specific optimization problems are depicted. The main topics covered in this book are: chrematistics of social networks, modeling of social influence propagation, popular research problems in social influence analysis such as influence maximization, rumor blocking, rumor source detection, and multiple social influence competing.
Keywords
Authors and Affiliations
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Department of Mathematics and Computer Science, Texas Woman’s University, Denton, USA
Wen Xu
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Department of Computer Science, University of Texas, Dallas, Richardson, USA
Weili Wu
Bibliographic Information
Book Title: Optimal Social Influence
Authors: Wen Xu, Weili Wu
Series Title: SpringerBriefs in Optimization
DOI: https://doi.org/10.1007/978-3-030-37775-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-37774-8Published: 30 January 2020
eBook ISBN: 978-3-030-37775-5Published: 29 January 2020
Series ISSN: 2190-8354
Series E-ISSN: 2191-575X
Edition Number: 1
Number of Pages: VIII, 124
Number of Illustrations: 5 b/w illustrations, 19 illustrations in colour
Topics: Optimization, Algorithm Analysis and Problem Complexity, Mathematical Modeling and Industrial Mathematics, Math Applications in Computer Science