Network analysis literacy: a practical approach to the analysis of networks
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Wien
Springer
[2016]
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| Schriftenreihe: | Lecture notes in social networks
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| Beschreibung: | 1 Online-Ressource (XXIII, 535 Seiten) Illustrationen |
| ISBN: | 9783709107416 |
| ISSN: | 2190-5436 |
| DOI: | 10.1007/978-3-7091-0741-6 |
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Datensatz im Suchindex
| _version_ | 1804176735256707072 |
|---|---|
| adam_text | Titel: Network analysis literacy
Autor: Zweig, Katharina A
Jahr: 2016
Contents
Part I Introduction
1 A First Encounter........................................ 3
1.1 Introduction to Network Analysis....................... 3
1.2 Data............................................. 5
1.2.1 From Relationship to Graph.................... 6
1.2.2 First Probes into the Data...................... 8
1.2.3 Measuring Indirect Effects...................... 12
1.2.4 Distributions................................ 13
1.3 Network Analysis Literacy: A Primer.................... 15
1.3.1 Visualizations............................... 15
1.4 Approaches to Network Analysis....................... 18
1.5 Outlook........................................... 19
1.6 Recommended Reading.............................. 20
References............................................... 21
2 Graph Theory, Social Network Analysis,
and Network Science...................................... 23
2.1 Introduction....................................... 23
2.2 The Basis......................................... 24
2.2.1 Graph Theory............................... 24
2.2.2 The Origins of Social Network Analysis
in Sociology................................ 27
2.2.3 Typical Viewpoints of Social Network Analysis..... 30
2.2.4 Network Science............................. 31
2.3 Universal Structures versus Individual Features............ 35
2.3.1 Statistical Physics and Early Complex Network
Analysis.................................... 37
2.3.2 Statistical Physics and Complex
Network Analysis............................ 38
2.3.3 Complex Network Analysis in Other Disciplines..... 40
XV
Contents
2.4 Network Analysis Literacy: General Requirements.......... 42
2.4.1 Implementations and Verbal Descriptions of
Network Analytic Measures: A Primer............ 42
2.4.2 Interpreting a Measure s Value: A Primer.......... 43
2.4.3 Interpretation by Trained Domain Experts.......... 45
2.4.4 Interpretation by Academic Experts............... 48
2.4.5 The Widespread Use of Scientific Rituals.......... 49
2.4.6 The Interpretation of Network Analytic Measures .... 49
2.5 Recommended Reading.............................. 53
2.6 Exercise.......................................... 53
References............................................... 53
Definitions.............................................. 57
3.1 Introduction....................................... 57
3.2 Mathematical Abbreviations........................... 58
3.3 Set Theoretic Terms................................. 58
3.3.1 Function................................... 60
3.3.2 Partitions and Hierarchical Clustering............. 60
3.4 Mathematical Operators.............................. 61
3.5 Graph Theoretic Definitions........................... 61
3.5.1 Distances in Graphs........................... 63
3.5.2 Degrees and Walks in Graphs................... 63
3.5.3 Graph Families.............................. 65
3.6 Data Structures for Graphs............................ 66
3.6.1 Basic Data Structures.......................... 67
3.6.2 Basic Data Structures for Simple Graphs........... 68
3.6.3 Data Structures and Definitions
for Directed Graphs........................... 71
3.6.4 Weighted Graphs............................. 72
3.6.5 Bipartite and Affiliation Networks................ 73
3.6.6 Multiplex Networks........................... 74
3.7 Graph File Formats.................................. 74
3.7.1 Graph Formats for Visualization................. 77
3.8 A Little Bit of Linear Algebra......................... 77
3.8.1 Scalar Product............................... 77
3.9 Normalization...................................... 78
3.9.1 Covariance.................................. 78
3.9.2 Correlation Coefficient......................... 79
3.10 Algorithms and Runtime Complexity.................... 80
3.11 Plots and Diagrams.................................. 81
3.12 Distributions....................................... 82
3.13 A Bit of Statistics................................... 82
3.14 Markov Chains..................................... 83
3.14.1 Properties of Markov Chains.................... 85
Contents xvii
3.15 Further Reading.................................... 86
3.16 Exercises.......................................... 86
References............................................... 88
Part II Methods
4 Classic Network Analytic Measures.......................... 91
4.1 Introduction....................................... 91
4.2 Direct Statistics..................................... 92
4.3 Distance Based Measures............................. 93
4.4 Degree Based Measures.............................. 95
4.4.1 Degree Distribution........................... 95
4.4.2 Assortativity................................ 95
4.5 Mutuality, Transitivity, and the Clustering Coefficient....... 99
4.5.1 Mutuality or Reciprocity....................... 99
4.5.2 Transitivity................................. 100
4.6 Density........................................... 102
4.7 Summary......................................... 104
4.8 Further Reading.................................... 105
4.9 Exercises.......................................... 105
References............................................... 107
5 Network Representations of Complex Systems................. 109
5.1 Introduction....................................... 109
5.2 Why Networks are only Models of Complex Systems....... 109
5.2.1 Edges as Abstract Representations of Real-World
Relationships................................ Ill
5.2.2 Types of Network Representations............... 113
5.3 Phases of a Network Analytic Project.................... 117
5.3.1 Trilemma of Complex Network Analysis.......... 119
5.4 Defining the Entity of Interest.......................... 121
5.4.1 Network Boundary........................... 122
5.4.2 Observing Entities............................ 123
5.4.3 Entity Resolution............................. 126
5.5 Relationships and Mathematical Relations................ 127
5.5.1 Classic Relationships Analyzed in Complex
Networks................................... 130
5.6 Weighted and Dynamic Graphs........................ 131
5.6.1 Observing and Representing Weighted
Relationships................................ 131
5.6.2 Dynamic Networks........................... 132
5.6.3 Transformation into Undirected, Unweighted
Networks................................... 133
5.7 One-Mode Projections of Bipartite Graphs................ 137
5.7.1 Classic One-Mode Projections................... 137
xvüi Contents
5.7.2 Show Case: Co-authorship Networks.............. 139
5.8 An Example: Metabolie Networks...................... 141
5.9 Summary......................................... 145
5.10 Further Reading.................................... 145
5.11 Exercises.......................................... 146
References............................................... 147
6 Random Graphs and Network Models....................... 149
6.1 Introduction....................................... 149
6.2 The Set of All Graphs with the Same Number of Nodes..... 150
6.2.1 The G(n,m) Random Graph Model............... 152
6.3 The Classic Random Graph Model...................... 154
6.4 The Small-World Model: Explaining the Small-World
Phenomenon....................................... 158
6.4.1 The Small-World Model (WS-Model)............. 162
6.5 The Preferential Attachment Model (BA-Model)........... 165
6.5.1 Scale-Freeness............................... 166
6.6 When is a Random Graph Model Explanatory?............ 170
6.7 Summary......................................... 174
6.8 Further Reading.................................... 175
6.9 Exercises.......................................... 177
References............................................... 179
7 Random Graphs as Null Models............................ 183
7.1 Introduction....................................... 183
7.2 Assessing the Significance of a Structural Feature.......... 183
7.2.1 Reciprocity Revisited I........................ 184
7.2.2 What is the Best Null Model for Assessing
Reciprocity in General?........................ 186
7.2.3 Node Similarity and Co-occurrence............... 187
7.3 Fixed and Expected Degree Sequence Models............. 191
7.3.1 Stub or Configuration Method................... 193
7.3.2 Simple Independence Model (SIM)—
Approximating the Configuration Model........... 194
7.3.3 Chung-Lu-Model: Expected Degree Sequences...... 196
7.3.4 Fixed Degree Sequence Model.................. 197
7.4 The Philosophy behind Identifying Statistically
Significant Structural Features.......................... 198
7.5 History of Assessing the Significance
of Real-World Network Structures...................... 201
7.5.1 Network Motifs.............................. 202
7.5.2 The Algorithm............................... 203
7.5.3 Biologically Meaningful Motifs.................. 206
7.5.4 Choosing the Best Null Model.................. 207
Contents xix
7.6 Summary......................................... 208
7.7 Further Reading.................................... 209
7.8 Exercises.......................................... 210
References............................................... 213
8 Understanding and Designing Network Measures............... 215
8.1 Introduction....................................... 215
8.2 Beware of verbal Descriptions—Why Mathematical
Equations are Necessary.............................. 216
8.2.1 Reciprocity................................. 218
8.3 Profile of a Measure s Behavior........................ 221
8.3.1 Applicability................................ 222
8.3.2 Range of the Measure and Extremal Graphs........ 225
8.3.3 Scalability.................................. 226
8.3.4 Size Independence/Comparability................ 227
8.3.5 Robustness.................................. 228
8.3.6 Assumptions................................ 228
8.4 How to Design a Network Analytic Measure.............. 230
8.4.1 Generalizing a Method........................ 231
8.4.2 Another Interpretation of the Degree
in Weighted Graphs........................... 235
8.4.3 Clustering Coefficient for Bipartite Graphs......... 235
8.5 Summary......................................... 238
8.6 Recommended Reading.............................. 238
8.7 Exercises.......................................... 239
References............................................... 241
9 Centrality Indices........................................ 243
9.1 Introduction....................................... 243
9.2 What is a Centrality Index?........................... 244
9.3 Classic Centrality Indices............................. 246
9.3.1 Degree-Like Centralities....................... 246
9.3.2 Closeness-Like Centralities..................... 249
9.3.3 Stress and betweenness-Like Centralities........... 250
9.3.4 Correlation between Different Centrality Indices..... 255
9.3.5 Comparing Centrality Values in Different
Networks................................... 256
9.3.6 The Centralization of a Graph................... 258
9.4 Generalizing Centrality Indices......................... 259
9.4.1 Centrality Indices for Networks between
Different Groups of Nodes...................... 259
9.4.2 Centrality Indices for Directed Networks........... 260
9.4.3 Centrality Indices for Weighted Networks.......... 260
9.5 Characterizations of Centrality Indices................... 261
9.5.1 The Graph-Theoretic Perspective................. 261
9.5.2 Network Flow Processes and Centrality Indices..... 264
x Contents
9.6 Centrality-Based Visualization of Graphs................. 264
9.7 Applications of Centrality Indices....................... 265
9.7.1 Centrality Distributions as General Structural
Descriptors.................................. 266
9.7.2 Correlation between Centrality Indices
and External Properties........................ 268
9.7.3 Centrality Indices as Process-Based Predictors...... 270
9.8 Summary......................................... 271
9.9 Further Reading.................................... 271
9.10 Exercises.......................................... 272
References............................................... 274
art III Literacy
0 Literacy: Data Quality, Entities, and Nodes................... 279
10.1 Introduction....................................... 279
10.2 Describing a Network Representation Transparently......... 280
10.3 Bad Data.......................................... 282
10.3.1 Bad Data: Protein-Protein Interaction Networks...... 282
10.3.2 Bad Data: BGP Routing Data................... 286
10.3.3 Inferred Transcription Network Data.............. 287
10.4 Network Boundary.................................. 289
10.4.1 When is a Node a Node....................... 289
10.5 Sampling Effects.................................... 293
10.5.1 Dynamic and Time-Thresholded Data............. 296
10.6 Evaluating Sampling Strategies......................... 297
10.6.1 Evaluating BGP/Traceroute Data................. 298
10.7 Data Biases........................................ 299
10.7.1 Data Biases in Protein-Protein Interaction Data...... 299
10.7.2 Data Biases in Surveys........................ 300
10.7.3 Estimating the Degree of a Node in a Network...... 302
10.8 Curating Complex Networks........................... 304
10.9 Summary......................................... 306
10.10 Further Reading.................................... 306
10.11 Exercises.......................................... 306
References............................................... 309
1 Literacy: Relationships and Relations........................ 313
11.1 Introduction....................................... 313
11.2 When is an Edge an Edge?............................ 314
11.3 Aggregations in Time and Space....................... 318
11.3.1 Aggregation in Time.......................... 318
11.3.2 Aggregation in Space......................... 319
11.3.3 Choosing an Appropriate Observation Period....... 320
Contents xxi
11.4 Weighted Relationships............................... 322
11.4.1 Interrelationship with Chosen Method............. 322
11.4.2 Dynamic Weights............................ 325
11.4.3 Thresholding................................ 326
11.5 Proxy Relationships................................. 327
11.5.1 Proxies for Sexual Relationship Networks.......... 327
11.5.2 Online Social Network Data as Proxies............ 329
11.5.3 With Whom do We Discuss Important Matter....... 330
11.5.4 Co-authorship versus Collaboration............... 332
11.5.5 Interchangeability of Social Relations............. 332
11.5.6 Observational versus Recalled Interactions......... 334
11.5.7 Email Interaction versus Communication
Networks................................... 334
11.5.8 Internet Network Data and Their Proxies........... 336
11.6 Relations that don t Lend Themselves to a Network
Representation..................................... 338
11.6.1 Information Contained in Relations............... 338
11.6.2 Mathematical Relations without Network
Processes................................... 340
11.6.3 Aggregating Paths into Complex Networks......... 341
11.6.4 Relationships, Network Processes, and Complex
Networks................................... 344
11.7 Horizons of Network Processes........................ 348
11.8 Data Responsibility.................................. 350
11.8.1 Evaluating Existing Network Data for Re-use....... 351
11.8.2 Data Hygiene, Producer and Consumer Rules....... 353
11.8.3 Producer Rules: Making Data Reusable............ 354
11.8.4 Consumer Rules: Validating Data................ 356
11.9 Aim of Analysis (A-Rules)............................ 357
11.9.1 Publishers Responsibility...................... 357
11.10 Summary......................................... 358
11.11 Further Reading.................................... 359
References............................................... 359
12 Literacy: When Is a Network Model Explanatory?............. 363
12.1 Introduction....................................... 363
12.2 Models of Networks and Processes...................... 365
12.2.1 What is a Scientific Model?..................... 366
12.2.2 Modelling Processes on Complex Networks........ 370
12.2.3 Evolution of Models.......................... 371
12.3 Structure, Function, and Behavior of Network Models....... 372
12.3.1 Interpretation of Smallness as a Function......... 373
12.3.2 Properties and Behavior of Scale-Free
Networks................................... 377
xxii Contents
12.4 Explanatory Models................................. 381
12.4.1 When Preferential Attachment is not Enough....... 382
12.4.2 Networks with a Scale-Free Degree Distribution
Which are not Scale-Free ..................... 383
12.4.3 The Internet—A Scale-Free Network without a
Hub-Dominated Architecture.................... 384
12.4.4 Shrinking Diameters in the Evolution of Complex
Networks................................... 385
12.4.5 Measuring Preferential Attachment............... 385
12.5 Summary......................................... 387
12.6 Further Reading.................................... 389
References............................................... 392
13 Literacy: Choosing the Best Null Model...................... 395
13.1 Introduction....................................... 395
13.2 Assessing the Small-World Phenomenon................. 398
13.2.1 Clustering Coefficient in One-Mode Projections
of Bipartite Graphs........................... 399
13.3 The Rich-Club Coefficient............................ 401
13.4 Reciprocity Revisited II.............................. 405
13.5 A New Perspective on One-Mode Projections............. 407
13.5.1 The Simple Independence Model SIM............. 408
13.5.2 An Example: MovieLens....................... 410
13.5.3 Discussion of the SIM......................... 415
13.5.4 The Fixed Degree Sequence Model FDSM for
Bipartite Graphs.............................. 418
13.6 Evaluating Expectation Models by a Gold Standard or
Ground Truth...................................... 419
13.6.1 Building the OMP............................ 420
13.6.2 Is There a Weighted FDSM?.................... 421
13.7 Can the Configuration Model Replace the FDSM?.......... 422
13.8 Summary......................................... 425
13.9 Further Reading.................................... 426
13.10 Exercises.......................................... 427
References............................................... 428
14 Literacy Interpretation.................................... 431
14.1 Introduction....................................... 431
14.2 The Interpretation of Measures in the Context
of a Complex System................................ 432
14.3 Interpretation of Distance-Based Measures................ 435
14.3.1 Robustness Measures Based on Distance........... 435
14.3.2 Comparing Average Distances of Different
Networks................................... 440
14.3.3 Interpretation of Low Average Distances
in Metabolic Networks........................ 441
Contents xxm
14.4 Centrality Index Literacy............................. 443
14.4.1 Borgatti s Flow Concept....................... 444
14.4.2 Interpretation of Classic Centrality Indices......... 445
14.4.3 Air Transportation Networks.................... 447
14.4.4 Multiplex Air-Transportation Networks............ 450
14.4.5 Designing Interprétable Centrality Indices.......... 454
14.5 Explorative Applications of Distance Based Measures....... 455
14.6 The Centrality of Moscow in the 12th and 13th Century..... 457
14.7 Sexual Contact Networks............................. 462
14.7.1 From Data to Network......................... 463
14.7.2 The Human Web of Sexual Contacts.............. 463
14.7.3 An Assessment of Preferential Attachment
as a Mechanism for Human Sexual Network
Formation.................................. 466
14.8 Post-Hoc Analysis................................... 467
14.9 Verbal Description of Findings......................... 469
14.10 Summary......................................... 470
14.11 Exercises.......................................... 471
References............................................... 472
15 Ethics in Network Analysis................................. 475
15.1 Why Ethical Network Analysis Needs Network Analysis
Literacy........................................... 475
15.2 The Wegman Report................................. 476
15.2.1 Discrediting a Scientist by Co-authorship-Network
Analysis.................................... 476
15.3 Who Owns a Relationship?............................ 479
15.4 Prediction Based on Network Analysis................... 482
15.5 Summary......................................... 483
References............................................... 484
Appendix A: The Structure and Typical Outlets of Network
Analytic Papers................................... 487
Appendix B: Glossary......................................... 493
Appendix C: Solutions........................................ 499
Author Index................................................ 529
Subject Index................................................ 531
|
| any_adam_object | 1 |
| author | Zweig, Katharina A. |
| author_GND | (DE-588)133283194 |
| author_facet | Zweig, Katharina A. |
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| author_sort | Zweig, Katharina A. |
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| building | Verbundindex |
| bvnumber | BV043857526 |
| classification_rvk | ST 650 |
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| ctrlnum | (ZDB-2-SCS)9783709107416 (OCoLC)962106564 (DE-599)BVBBV043857526 |
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| dewey-hundreds | 000 - Computer science, information, general works |
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| dewey-raw | 004 |
| dewey-search | 004 |
| dewey-sort | 14 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-3-7091-0741-6 |
| format | Electronic eBook |
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| id | DE-604.BV043857526 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:36:54Z |
| institution | BVB |
| isbn | 9783709107416 |
| issn | 2190-5436 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029267702 |
| oclc_num | 962106564 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-523 DE-859 DE-473 DE-BY-UBG DE-29 DE-20 DE-92 DE-573 DE-M347 DE-703 DE-739 DE-634 DE-91 DE-BY-TUM |
| owner_facet | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-523 DE-859 DE-473 DE-BY-UBG DE-29 DE-20 DE-92 DE-573 DE-M347 DE-703 DE-739 DE-634 DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (XXIII, 535 Seiten) Illustrationen |
| psigel | ZDB-2-SCS ZDB-30-PQE ZDB-2-SCS_2016 ZDB-30-PQE TUM_Einzelkauf |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Springer |
| record_format | marc |
| series2 | Lecture notes in social networks |
| spelling | Zweig, Katharina A. Verfasser (DE-588)133283194 aut Network analysis literacy a practical approach to the analysis of networks Katharina A. Zweig Wien Springer [2016] 1 Online-Ressource (XXIII, 535 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Lecture notes in social networks 2190-5436 Computer science Data mining Application software Complexity, Computational Computer Science Computer Appl. in Social and Behavioral Sciences Applications of Graph Theory and Complex Networks Complexity Data-driven Science, Modeling and Theory Building Data Mining and Knowledge Discovery Netzwerkanalyse (DE-588)4075298-7 gnd rswk-swf Netzwerkanalyse (DE-588)4075298-7 s DE-604 Erscheint auch als Druck-Ausgabe 978-3-7091-0740-9 (DE-604)BV044282819 https://doi.org/10.1007/978-3-7091-0741-6 Verlag URL des Erstveröffentlichers Volltext HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029267702&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zweig, Katharina A. Network analysis literacy a practical approach to the analysis of networks Computer science Data mining Application software Complexity, Computational Computer Science Computer Appl. in Social and Behavioral Sciences Applications of Graph Theory and Complex Networks Complexity Data-driven Science, Modeling and Theory Building Data Mining and Knowledge Discovery Netzwerkanalyse (DE-588)4075298-7 gnd |
| subject_GND | (DE-588)4075298-7 |
| title | Network analysis literacy a practical approach to the analysis of networks |
| title_auth | Network analysis literacy a practical approach to the analysis of networks |
| title_exact_search | Network analysis literacy a practical approach to the analysis of networks |
| title_full | Network analysis literacy a practical approach to the analysis of networks Katharina A. Zweig |
| title_fullStr | Network analysis literacy a practical approach to the analysis of networks Katharina A. Zweig |
| title_full_unstemmed | Network analysis literacy a practical approach to the analysis of networks Katharina A. Zweig |
| title_short | Network analysis literacy |
| title_sort | network analysis literacy a practical approach to the analysis of networks |
| title_sub | a practical approach to the analysis of networks |
| topic | Computer science Data mining Application software Complexity, Computational Computer Science Computer Appl. in Social and Behavioral Sciences Applications of Graph Theory and Complex Networks Complexity Data-driven Science, Modeling and Theory Building Data Mining and Knowledge Discovery Netzwerkanalyse (DE-588)4075298-7 gnd |
| topic_facet | Computer science Data mining Application software Complexity, Computational Computer Science Computer Appl. in Social and Behavioral Sciences Applications of Graph Theory and Complex Networks Complexity Data-driven Science, Modeling and Theory Building Data Mining and Knowledge Discovery Netzwerkanalyse |
| url | https://doi.org/10.1007/978-3-7091-0741-6 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029267702&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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