Statistical analysis of network data with R:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY [u.a.]
Springer
2014
|
| Schriftenreihe: | Use R!
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 207 S. Ill., graph. Darst. |
| ISBN: | 9781493909827 |
Internformat
MARC
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| 100 | 1 | |a Kolaczyk, Eric D. |d 1968- |e Verfasser |0 (DE-588)106893459X |4 aut | |
| 245 | 1 | 0 | |a Statistical analysis of network data with R |c Eric D. Kolaczyk ; Gábor Csárdi |
| 264 | 1 | |a New York, NY [u.a.] |b Springer |c 2014 | |
| 300 | |a XIII, 207 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Use R! | |
| 650 | 0 | 7 | |a Statistisches Modell |0 (DE-588)4121722-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Netzwerk |0 (DE-588)4171529-9 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Netzwerk |0 (DE-588)4171529-9 |D s |
| 689 | 0 | 1 | |a Statistisches Modell |0 (DE-588)4121722-6 |D s |
| 689 | 0 | |C b |5 DE-604 | |
| 700 | 1 | |a Csárdi, Gábor |d 1976- |e Verfasser |0 (DE-588)1051887496 |4 aut | |
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| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027362389&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-027362389 | |
Datensatz im Suchindex
| _version_ | 1817681164121407488 |
|---|---|
| adam_text |
Contents
Introduction
. 1
1.1
Why Networks?
. 1
1.2
Types of Network Analysis
. 3
1.2.1
Visualizing and Characterizing Networks
. 3
1.2.2
Network Modeling and Inference
. 5
1.2.3
Network Processes
. 7
1.3
Why Use
R
for Network Analysis?
. 8
1.4
About This Book
. 9
1.5
About the
R
code
. 10
Manipulating Network Data
. 13
2.1
Introduction
. 13
2.2
Creating Network Graphs
. 14
2.2.1
Undirected and Directed Graphs
. 14
2.2.2
Representations for Graphs
. 16
2.2.3
Operations on Graphs
. 17
2.3
Decorating Network Graphs
. 18
2.3.1
Vertex. Edge, and Graph Attributes
. 18
2.3.2
Using Data Frames
. 20
2.4
Talking About Graphs
. 21
2.4.1
Basic Graph Concepts
. 21
2.4.2
Special Types of Graphs
. 24
2.5
Additional Reading
. 28
Visualizing Network Data
. 29
3.1
Introduction
. 29
3.2
Elements of Graph Visualization
. 29
3.3
Graph Layouts
. 30
3.4
Decorating Graph Layouts
. 34
3.5
Visualizing Large Networks
. 36
IX
Contents
3.6
Using Visualization Tools Outside of
R
. 39
3.7
Additional Reading
.
41
4
Descriptive Analysis of Network Graph Characteristics
. 43
4.1
Introduction
.
4^
4.2
Vertex and Edge Characteristics
. 44
4.2.1
Vertex Degree
. 44
4.2.2
Vertex Centrality
. 47
4.2.3
Characterizing Edges
. 50
4.3
Characterizing Network Cohesion
. 52
4.3.1
Subgraphs and Censuses
. 52
4.3.2
Density and Related Notions of Relative Frequency
. 55
4.3.3
Connectivity, Cuts, and Flows
. 57
4.4
Graph Partitioning
. 59
4.4.1
Hierarchical Clustering
. 60
4.4.2
Spectral Partitioning
. 62
4.4.3
Validation of Graph Partitioning
. 64
4.5
Assortativity and Mixing
. 66
4.6
Additional Reading
. 67
5
Mathematical Models for Network Graphs
. 69
5.
1 Introduction
. 69
5.2
Classical Random Graph Models
. 70
5.3
Generalized Random Graph Models
. 72
5.4
Network Graph Models Based on Mechanisms
. 74
5.4.1
Small-World Models
. 74
5.4.2
Preferential Attachment Models
. 77
5.5
Assessing Significance of Network Graph Characteristics
. 79
5.5.
1 Assessing the Number of Communities in a Network
. 79
5.5.2
Assessing Small World Properties
. 81
5.6
Additional Reading
. 83
*
Statistical Models
Гот
Network Graphs
. 85
6.1
Introduction
. 85
6.2
Exponential Random Graph Models
. 86
6.2.1
Genera] Formulation
. 86
6.2.2
Specifying a Model
. 88
6.2.3
Model Fitting
. 92
6.2.4
Goodness-of-Fu
. 94
6.3
Network Block Models
.
.У.
. 95
6.3.1
Model Specification
. %
63.2
Model Fining
.]. * * " * 97
6.3.3
Goodness-of-FU
.
IOO
6.4
Latent Network Models
.
IIIIIIIIIIIIIIIIIIIIIIIIII
102
6.4.
1 General Formulation
.
IO2
Contents xi
6.4.2
Specifying the Latent Effects
.103
6.4.3
Model Fitting
.104
6.4.4
Goodness-of-Fit
.107
6.5
Additional Reading
.109
Network Topology Inference
.
7.1
Introduction
.
7.2
Link Prediction
.
7.3
Association Network Inference
11
11
12
16
18
7.3.1
Correlation Networks
.
7.3.2
Partial Correlation Networks
.122
7.3.3
Gaussian Graphical Model Networks
.126
7.4 Tomographie
Network Topology Inference
.129
7.4.1
Constraining the Problem: Tree Topologies
.129
7.4.2 Tomographie
Inference of Tree Topologies:
An Illustration
.132
7.5
Additional Reading
.134
8
Modeling and Prediction for Processes on Network Graphs
.135
8.1
Introduction
.135
8.2
Nearest Neighbor Methods
.136
8.3
Markov Random Fields
.141
8.3.1
General Characterization
.141
8.3.2
Auto-Logistic Models
.142
8.3.3
Inference and Prediction for Auto-logistic Models
.144
8.3.4
Goodness of Fit
.147
8.4
Kernel Methods
.148
8.4.1
Designing Kernels on Graphs
.148
8.4.2
Kernel Regression on Graphs
.152
8.5
Modeling and Prediction for Dynamic Processes
.154
8.5.1
Epidemic Processes: An Illustration
.154
8.6
Additional Reading
.158
9
Analysis of Network Flow Data
.161
9.1
Introduction
.161
9.2
Modeling Network Rows: Gravity Models
.162
9.2.1
Model Specification
.162
9.2.2
Inference for Gravity Models
.166
9.3
Predicting Network Rows: Traffic Matrix Estimation
.170
9.3.1
An Ill-Posed Inverse Problem
.171
9.3.2
The Tomogravity Method
.175
9 4
Additional Reading
.178
xii Contents
IO
Dynamic Networks.179
10.1
Introduction.
179
10.2
Representation
and Manipulation of Dynamic Networks
.180
10.3
Visualization of Dynamic Networks
.186
10.4
Characterization of Dynamic Networks
.191
10.5
Modeling Dynamic Networks
.194
References
.197
Index
.205 |
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| ctrlnum | (OCoLC)881854980 (DE-599)BVBBV041918824 |
| dewey-full | 003.015195 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 003 - Systems |
| dewey-raw | 003.015195 |
| dewey-search | 003.015195 |
| dewey-sort | 13.015195 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Soziologie Mathematik Wirtschaftswissenschaften |
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| spelling | Kolaczyk, Eric D. 1968- Verfasser (DE-588)106893459X aut Statistical analysis of network data with R Eric D. Kolaczyk ; Gábor Csárdi New York, NY [u.a.] Springer 2014 XIII, 207 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Use R! Statistisches Modell (DE-588)4121722-6 gnd rswk-swf Netzwerk (DE-588)4171529-9 gnd rswk-swf Netzwerk (DE-588)4171529-9 s Statistisches Modell (DE-588)4121722-6 s b DE-604 Csárdi, Gábor 1976- Verfasser (DE-588)1051887496 aut Erscheint auch als Online-Ausgabe 978-1-4939-0983-4 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027362389&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kolaczyk, Eric D. 1968- Csárdi, Gábor 1976- Statistical analysis of network data with R Statistisches Modell (DE-588)4121722-6 gnd Netzwerk (DE-588)4171529-9 gnd |
| subject_GND | (DE-588)4121722-6 (DE-588)4171529-9 |
| title | Statistical analysis of network data with R |
| title_auth | Statistical analysis of network data with R |
| title_exact_search | Statistical analysis of network data with R |
| title_full | Statistical analysis of network data with R Eric D. Kolaczyk ; Gábor Csárdi |
| title_fullStr | Statistical analysis of network data with R Eric D. Kolaczyk ; Gábor Csárdi |
| title_full_unstemmed | Statistical analysis of network data with R Eric D. Kolaczyk ; Gábor Csárdi |
| title_short | Statistical analysis of network data with R |
| title_sort | statistical analysis of network data with r |
| topic | Statistisches Modell (DE-588)4121722-6 gnd Netzwerk (DE-588)4171529-9 gnd |
| topic_facet | Statistisches Modell Netzwerk |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027362389&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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