Overview
- Contains state-of-the-art methodologies for community detection
- Features prediction techniques based on social network analysis
- Includes detailed tables, illustrative figures, and techniques for graph analysis
Part of the book series: Lecture Notes in Social Networks (LNSN)
Included in the following conference series:
Conference proceedings info: ASONAM 2017.
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book focuses on novel and state-of-the-art scientific work in the area of detection and prediction techniques using information found generally in graphs and particularly in social networks. Community detection techniques are presented in diverse contexts and for different applications while prediction methods for structured and unstructured data are applied to a variety of fields such as financial systems, security forums, and social networks. The rest of the book focuses on graph-based techniques for data analysis such as graph clustering and edge sampling.
The research presented in this volume was selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. This book will appeal to practitioners, researchers and students in the field.
Editors and Affiliations
Bibliographic Information
Book Title: From Security to Community Detection in Social Networking Platforms
Editors: Panagiotis Karampelas, Jalal Kawash, Tansel Özyer
Series Title: Lecture Notes in Social Networks
DOI: https://doi.org/10.1007/978-3-030-11286-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-11285-1Published: 10 April 2019
eBook ISBN: 978-3-030-11286-8Published: 09 April 2019
Series ISSN: 2190-5428
Series E-ISSN: 2190-5436
Edition Number: 1
Number of Pages: X, 237
Number of Illustrations: 28 b/w illustrations, 70 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Computational Social Sciences, Big Data/Analytics, Computer Appl. in Social and Behavioral Sciences, Complex Systems