Social networks with rich edge semantics:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton
CRC Press
[2017]
|
| Schriftenreihe: | Chapman & Hall/CRC data mining and knowledge discovery series
|
| Schlagworte: | |
| Online-Zugang: | Klappentext Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xx, 210 Seiten Illustrationen, Diagramme |
| ISBN: | 9781138032439 |
Internformat
MARC
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Preface xi
List of Figures xiii
List of Tables xvii
Glossary xix
1 Introduction 1
1.1 What is a social network?................................... 1
1.2 Multiple aspects of relationships .......................... 6
1.3 Formally representing social networks....................... 7
2 The core model 9
2.1 Representing networks to understand their structures........ 9
2.2 Building layered models.................................. 11
2.3 Summary.................................................... 16
3 Background 17
3.1 Graph theory background ................................... 17
3.2 Spectral graph theory...................................... 18
3.2.1 The unnormalized graph Laplacian............... 21
3.2.2 The normalized graph Laplacians................ 23
3.3 Spectral pipeline.......................................... 24
3.4 Spectral approaches to clustering.......................... 24
3.4.1 Undirected spectral clustering algorithms...... 26
3.4.2 Which Laplacian clustering should be used? ... 27
3.5 Summary.................................................... 28
4 Modelling relationships of different types 31
4.1 Typed edge model approach.................................. 32
4.2 Typed edge spectral embedding.............................. 32
4.3 Applications of typed networks............................. 34
4.4 Summary.................................................. 37
vii
viii
Contents
5 Modelling asymmetric relationships 41
5.1 Conventional directed spectral graph embedding............... 41
5.2 Directed edge layered approach............................... 44
5.2.1 Validation of the new directed embedding... 46
5.2.2 SVD computation for the directed edge model
approach........................................ 47
5.3 Applications of directed networks............................ 48
5.4 Summary...................................................... 67
6 Modelling asymmetric relationships with multiple types 69
6.1 Combining directed and typed embeddings...................... 69
6.2 Layered approach and compositions ........................... 70
6.3 Applying directed typed embeddings........................... 72
6.3.1 Florentine families........................ 72
6.3.2 Criminal groups ................................. 74
6.4 Summary...................................................... 78
7 Modelling relationships that change over time 81
7.1 Temporal networks ........................................... 81
7.2 Applications of temporal networks............................ 85
7.2.1 The undirected network over time........... 85
7.2.2 The directed network over time............. 89
7.3 Summary...................................................... 94
8 Modelling positive and negative relationships 97
8.1 Signed Laplacian ........................................... 97
8.2 Unnormalized spectral Laplacians of signed graphs............ 98
8.2.1 Rayleigh quotients of signed unnormalized
Laplacians....................................... 99
8.2.2 Graph cuts of signed unnormalized Laplacians . . 100
8.3 Normalized spectral Laplacians of signed graphs............. 102
8.3.1 Rayleigh quotients of signed random-walk
Laplacians..................................... 102
8.3.2 Graph cuts of signed random-walk Laplacians . . 104
8.4 Applications of signed networks .............................105
8.5 Summary..................................................... 118
9 Signed graph-based semi-supervised learning 121
9.1 Approach.................................................... 122
9.2 Problems of imbalance in graph data........................ 127
9.3 Summary..................................................... 137
10 Combining directed and signed embeddings 139
10.1 Composition of directed and signed layer models............. 139
10.2 Application to signed directed networks .................... 142
10.2.1 North and West Africa conflict............ 143
Contents_____________________________________________________________tx
10.3 Extensions to other compositions....................... 152
10.4 Summary................................................ 155
11 Summary 157
Appendices 161
A RatioCut consistency with two versions of each node 163
B NCut consistency with multiple versions of each node 167
C Signed unnormalized clustering 175
D Signed normalized Laplacian Lsns clustering 177
181
E Signed normalized Laplacian Lbns clustering
F Example MATLAB functions 183
199
Bibliography
t 209
Index
Social Networks with Rich Edge Semantics introduces a new mechanism for rep-
resenting 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 mot vated 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, sta-
tistical learning, and related areas, this book will help to provide a deeper under-
standing of real-world social networks.
|
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| language | English |
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| physical | xx, 210 Seiten Illustrationen, Diagramme |
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| spelling | Zheng, Quan (DE-588)1147709548 aut Social networks with rich edge semantics Quan Zheng, David Skillicorn Boca Raton CRC Press [2017] xx, 210 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC data mining and knowledge discovery series Includes bibliographical references and index Semantic Web Social media Semantic Web (DE-588)4688372-1 gnd rswk-swf Social Media (DE-588)4639271-3 gnd rswk-swf Social Media (DE-588)4639271-3 s Semantic Web (DE-588)4688372-1 s DE-604 Skillicorn, David aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030107840&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030107840&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zheng, Quan Skillicorn, David Social networks with rich edge semantics Semantic Web Social media Semantic Web (DE-588)4688372-1 gnd Social Media (DE-588)4639271-3 gnd |
| subject_GND | (DE-588)4688372-1 (DE-588)4639271-3 |
| title | Social networks with rich edge semantics |
| title_auth | Social networks with rich edge semantics |
| title_exact_search | Social networks with rich edge semantics |
| title_full | Social networks with rich edge semantics Quan Zheng, David Skillicorn |
| title_fullStr | Social networks with rich edge semantics Quan Zheng, David Skillicorn |
| title_full_unstemmed | Social networks with rich edge semantics Quan Zheng, David Skillicorn |
| title_short | Social networks with rich edge semantics |
| title_sort | social networks with rich edge semantics |
| topic | Semantic Web Social media Semantic Web (DE-588)4688372-1 gnd Social Media (DE-588)4639271-3 gnd |
| topic_facet | Semantic Web Social media Social Media |
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