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E-raamat: Social Networks with Rich Edge Semantics

(Queen's University, Canada), (School of Computing, Queen's University, Canada)
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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.
Preface xi
List of Figures
xiii
List of Tables
xvii
Glossary xix
1 Introduction
1(8)
1.1 What is a social network?
1(5)
1.2 Multiple aspects of relationships
6(1)
1.3 Formally representing social networks
7(2)
2 The core model
9(8)
2.1 Representing networks to understand their structures
9(2)
2.2 Building layered models
11(5)
2.3 Summary
16(1)
3 Background
17(14)
3.1 Graph theory background
17(1)
3.2 Spectral graph theory
18(6)
3.2.1 The unnormalized graph Laplacian
21(2)
3.2.2 The normalized graph Laplacians
23(1)
3.3 Spectral pipeline
24(1)
3.4 Spectral approaches to clustering
24(4)
3.4.1 Undirected spectral clustering algorithms
26(1)
3.4.2 Which Laplacian clustering should be used?
27(1)
3.5 Summary
28(3)
4 Modelling relationships of different types
31(10)
4.1 Typed edge model approach
32(1)
4.2 Typed edge spectral embedding
32(2)
4.3 Applications of typed networks
34(3)
4.4 Summary
37(4)
5 Modelling asymmetric relationships
41(28)
5.1 Conventional directed spectral graph embedding
41(3)
5.2 Directed edge layered approach
44(4)
5.2.1 Validation of the new directed embedding
46(1)
5.2.2 SVD computation for the directed edge model approach
47(1)
5.3 Applications of directed networks
48(19)
5.4 Summary
67(2)
6 Modelling asymmetric relationships with multiple types
69(12)
6.1 Combining directed and typed embeddings
69(1)
6.2 Layered approach and compositions
70(2)
6.3 Applying directed typed embeddings
72(6)
6.3.1 Florentine families
72(2)
6.3.2 Criminal groups
74(4)
6.4 Summary
78(3)
7 Modelling relationships that change over time
81(16)
7.1 Temporal networks
81(4)
7.2 Applications of temporal networks
85(9)
7.2.1 The undirected network over time
85(4)
7.2.2 The directed network over time
89(5)
7.3 Summary
94(3)
8 Modelling positive and negative relationships
97(24)
8.1 Signed Laplacian
97(1)
8.2 Unnormalized spectral Laplacians of signed graphs
98(4)
8.2.1 Rayleigh quotients of signed unnormalized Laplacians
99(1)
8.2.2 Graph cuts of signed unnormalized Laplacians
100(2)
8.3 Normalized spectral Laplacians of signed graphs
102(3)
8.3.1 Rayleigh quotients of signed random-walk Laplacians
102(2)
8.3.2 Graph cuts of signed random-walk Laplacians
104(1)
8.4 Applications of signed networks
105(13)
8.5 Summary
118(3)
9 Signed graph-based semi-supervised learning
121(18)
9.1 Approach
122(5)
9.2 Problems of imbalance in graph data
127(10)
9.3 Summary
137(2)
10 Combining directed and signed embeddings
139(18)
10.1 Composition of directed and signed layer models
139(3)
10.2 Application to signed directed networks
142(10)
10.2.1 North and West Africa conflict
143(9)
10.3 Extensions to other compositions
152(3)
10.4 Summary
155(2)
11 Summary
157(4)
Appendices
161(38)
A RatioCut consistency with two versions of each node
163(4)
B NCut consistency with multiple versions of each node
167(8)
C Signed unnormalized clustering
175(2)
D Signed normalized Laplacian Lsns clustering
177(4)
E Signed normalized Laplacian Lbns clustering
181(2)
F Example MATLAB functions
183(16)
Bibliography 199(10)
Index 209
David Skillicorn is a professor in the School of Computing at Queen's University. His undergraduate degree is from the  University of Sydney and his Ph.D. from the University of Manitoba. He has published extensively in the area of adversarial data analytics, including his recent books "Understanding High-Dimensional Spaces" and "Knowledge Discovery for Counterterrorism and Law Enforcement". He has also been involved in interdisciplinary research on radicalisation, terrorism, and financial fraud. He consults for the intelligence and security arms of government in several countries, and appears frequently in the media to comment on cybersecurity and terrorism.



Dr. Quan Zheng got his Ph.D. is in the School of Computing from Queens University in the year 2016.He has a Masters degree in Applied Mathematics with a specialization in statistics from Indiana University of Pennsylvania, and a Masters degree in Computer Science from the University of Ulm, and an undergraduate degree from Darmstadt University of Applied Science.



His research interests are in data mining and behavior analysis, particularly social network modeling and graph-based data analysis. He has proposed a few graph algorithms for identifying interested individuals and links, clustering and classification.
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