Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets.
Features
- Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time
- Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed
- Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate
- Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node
- Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups
Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks.
Conditions of Use
This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Social Networks with Rich Edge Semantics for free.
- Title
- Social Networks with Rich Edge Semantics
- Publisher
- CRC Press
- Author(s)
- David Skillicorn, Quan Zheng
- Published
- 2020-06-30
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 230
- Language
- English
- ISBN-10
- 0367573253
- ISBN-13
- 9780367573256
- License
- CC BY-NC-SA
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
Cover Half title Published titles Title Copyright Contents Preface List of figures List of tables Glossary Chapter 1 introduction 1.1 what is a social network 1.2 multiple aspects of relationships 1.3 formally representing social networks Chapter 2 the core model 2.1 representing networks to understand their structures 2.2 building layered models 2.3 summary Chapter 3 background 3.1 graph theory background 3.2 spectral graph theory 3.2.1 the unnormalized graph laplacian 3.2.2 the normalized graph laplacians 3.3 spectral pipeline 3.4 spectral approaches to clustering 3.4.1 undirected spectral clustering algorithms 3.4.2 which laplacian clustering should be used 3.5 summary Chapter 4 modelling relationships of different types 4.1 typed edge model approach 4.2 typed edge spectral embedding 4.3 applications of typed networks 4.4 summary Chapter 5 modelling asymmetric relationships 5.1 conventional directed spectral graph embedding 5.2 directed edge layered approach 5.2.1 validation of the new directed embedding 5.2.2 svd computation for the directed edge model approach 5.3 applications of directed networks 5.4 summary Chapter 6 modelling asymmetric relationships with multiple types 6.1 combining directed and typed embeddings 6.2 layered approach and compositions 6.3 applying directed typed embeddings 6.3.1 florentine families 6.3.2 criminal groups 6.4 summary Chapter 7 modelling relationships that change over time 7.1 temporal networks 7.2 applications of temporal networks 7.2.1 the undirected network over time 7.2.2 the directed network over time 7.3 summary Chapter 8 modelling positive and negative relationships 8.1 signed laplacian 8.2 unnormalized spectral laplacians of signed graphs 8.2.1 rayleigh quotients of signed unnormalized laplacians 8.2.2 graph cuts of signed unnormalized laplacians 8.3 normalized spectral laplacians of signed graphs 8.3.1 rayleigh quotients of signed random-walk laplacians 8.3.2 graph cuts of signed random-walk laplacians 8.4 applications of signed networks 8.5 summary Chapter 9 signed graph-based semi-supervised learning 9.1 approach 9.2 problems of imbalance in graph data 9.3 summary Chapter 10 combining directed and signed embeddings 10.1 composition of directed and signed layer models 10.2 application to signed directed networks 10.2.1 north and west africa conflict 10.3 extensions to other compositions 10.4 summary Chapter 11 summary Appendices Appendix A ratiocut consistency with two versions of each node Appendix B ncut consistency with multiple versions of each node Appendix C signed unnormalized clustering Appendix D signed normalized laplacian lsns clustering Appendix E signed normalized laplacian lbns clustering Appendix F example matlab functions Bibliography Index
