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E-raamat: Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution

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Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network EvolutionSuurem pilt
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This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved. Reviews: "this book is easy to read and entertaining, and much can be learned from it. Even if you know just about everything about large-scale and temporal networks, the book is a worthwhile read; you will learn a lot about SNA literature, patents, the US Supreme Court, and European soccer." (Social Networks)

"a clear and accessible textbook, balancing symbolic maths, code, and visual explanations. The authors enthusiasm for the subject matter makes it enjoyable to read" (JASSS)
Preface xiii
1 Temporal and Spatial Networks
1(17)
1.1 Modern Social Network Analysis
1(2)
1.2 Network Sizes
3(1)
1.3 Substantive Concerns
3(7)
1.3.1 Citation Networks
3(4)
1.3.2 Other Types of Large Networks
7(3)
1.4 Computational Methods
10(2)
1.5 Data for Large Temporal Networks
12(4)
1.5.1 The Main Datasets
12(2)
1.5.2 Secondary Datasets
14(2)
1.6 Induction and Deduction
16(2)
2 Foundations of Methods for Large Networks
18(51)
2.1 Networks
18(4)
2.1.1 Descriptions of Networks
20(1)
2.1.2 Degrees
21(1)
2.1.3 Descriptions of Properties
21(1)
2.1.4 Visualizations of Properties
22(1)
2.2 Types of Networks
22(6)
2.2.1 Temporal Networks
23(2)
2.2.2 Multirelational Networks
25(3)
2.2.3 Two-mode Networks
28(1)
2.3 Large Networks
28(4)
2.3.1 Small and Middle Sized Networks
29(1)
2.3.2 Large Networks
30(1)
2.3.3 Complexity of Algorithms
30(2)
2.4 Strategies for Analyzing Large Networks
32(1)
2.5 Statistical Network Measures
33(4)
2.5.1 Using Pajek and R Together
35(1)
2.5.2 Fitting Distributions
35(2)
2.6 Subnetworks
37(9)
2.6.1 Clusters, Clusterings, Partitions, Hierarchies
37(1)
2.6.2 Contractions of Clusters
38(2)
2.6.3 Subgraphs
40(2)
2.6.4 Cuts
42(4)
2.7 Connectivity Properties of Networks
46(7)
2.7.1 Walks
46(1)
2.7.2 Equivalence Relations and Partitions
47(1)
2.7.3 Connectivity
48(1)
2.7.4 Condensation
49(1)
2.7.5 Bow-tie Structure of the Web Graph
50(1)
2.7.6 The Internal Structure of Strong Components
51(1)
2.7.7 Bi-connectivity and k-connectivity
51(2)
2.8 Triangular and Short Cycle Connectivities
53(1)
2.9 Islands
54(3)
2.9.1 Defining Islands
55(1)
2.9.2 Some Properties of Islands
56(1)
2.10 Cores and Generalized Cores
57(4)
2.10.1 Cores
58(1)
2.10.2 Generalized Cores
59(2)
2.11 Important Vertices in Networks
61(7)
2.11.1 Degrees, Closeness, Betweenness and Other Indices
63(2)
2.11.2 Clustering
65(1)
2.11.3 Computing Further Indices Through Functions
66(2)
2.12 Transition to Methods for Large Networks
68(1)
3 Methods for Large Networks
69(48)
3.1 Acyclic Networks
71(4)
3.1.1 Some Basic Properties of Acyclic Networks
71(1)
3.1.2 Compatible Numberings: Depth and Topological Order
72(2)
3.1.3 Topological Orderings and Functions on Acyclic Networks
74(1)
3.2 SPC Weights in Acyclic Networks
75(6)
3.2.1 Citation Networks
75(1)
3.2.2 Analysis of Citation Networks
76(1)
3.2.3 Search Path Count Method
77(1)
3.2.4 Computing SPLC and SPNP Weights
77(1)
3.2.5 Implementation Details
78(1)
3.2.6 Vertex Weights
78(1)
3.2.7 General Properties of Weights
79(1)
3.2.8 SPC Weights
80(1)
3.3 Probabilistic Flow in Acyclic Network
81(1)
3.4 Nonacyclic Citation Networks
82(2)
3.5 Two-mode Networks from Data Tables
84(4)
3.5.1 Multiplication of Two-mode Networks
85(3)
3.6 Bibliographic Networks
88(6)
3.6.1 Co-authorship Networks
88(1)
3.6.2 Collaboration Networks
89(3)
3.6.3 Other Derived Networks
92(2)
3.7 Weights
94(2)
3.7.1 Normalizations of Weights
94(1)
3.7.2 k-Rings
94(1)
3.7.3 4-Rings and Analysis of Two-mode Networks
95(1)
3.7.4 Two-mode Cores
96(1)
3.8 Pathfinder
96(6)
3.8.1 Pathfinder Algorithms
100(1)
3.8.2 Computing the Closure Over the Pathfinder Semiring
101(1)
3.8.3 Spanish Algorithms
101(1)
3.8.4 A Sparse Network Algorithm
102(1)
3.9 Clustering, Blockmodeling, and Community Detection
102(1)
3.9.1 The Louvain Method and VOS
102(1)
3.10 Clustering Symbolic Data
103(4)
3.10.1 Symbolic Objects Described with Distributions
103(2)
3.10.2 The Leaders Method
105(2)
3.10.3 An Agglomerative Method
107(1)
3.11 Approaches to Temporal Networks
107(7)
3.11.1 Journeys -- Walks in Temporal Networks
108(2)
3.11.2 Measures
110(1)
3.11.3 Problems and Algorithms
111(3)
3.11.4 Evolution
114(1)
3.12 Levels of Analysis
114(2)
3.13 Transition to Substantive Topics
116(1)
4 Scientific Citation and Other Bibliographic Networks
117(58)
4.1 The Centrality Citation Network
117(1)
4.2 Preliminary Data Analyses
118(10)
4.2.1 Temporal Distribution of Publications
119(2)
4.2.2 Degree Distributions of the Centrality Literature
121(3)
4.2.3 Types of Works
124(2)
4.2.4 The Boundary Problem
126(2)
4.3 Transforming a Citation Network into an Acyclic Network
128(6)
4.3.1 Checking for the Presence of Cycles
128(5)
4.3.2 Dealing with Cycles in Citation Networks
133(1)
4.4 The Most Important Works
134(1)
4.5 SPC Weights
134(5)
4.5.1 Obtaining SPC Weights and Drawing Main Paths
135(1)
4.5.2 The Main Path of the Centrality Citation Network
135(4)
4.6 Line Cuts
139(2)
4.7 Line Islands
141(14)
4.7.1 The Main Island
143(2)
4.7.2 A Geophysics and Meteorology Line Island
145(5)
4.7.3 An Optical Network Line Island
150(4)
4.7.4 A Partial Summary of Main Path and Line Island Results
154(1)
4.8 Other Relevant Subnetworks for a Bounded Network
155(2)
4.9 Collaboration Networks
157(3)
4.9.1 Macros for Collaboration Networks
158(1)
4.9.2 An Initial Attempt of Analyses of Collaboration Networks
159(1)
4.10 A Brief Look at the SNA Literature SN5 Networks
160(13)
4.11 On the Centrality and SNA Collaboration Networks
173(2)
References
173(2)
5 Citation Patterns in Temporal United States Patent Data
175(41)
5.1 Patents
175(4)
5.2 Supreme Court Decisions Regarding Patents
179(4)
5.2.1 Co-cited Decisions
179(3)
5.2.2 Citations Between Co-cited Decisions
182(1)
5.3 The 1976--2006 Patent Data
183(1)
5.4 Structural Variables Through Time
184(4)
5.4.1 Temporally Specific Networks
184(2)
5.4.2 Shrinking Specific Patent Citation Networks
186(1)
5.4.3 Structural Properties
187(1)
5.5 Some Patterns of Technological Development
188(5)
5.5.1 Structural Properties of Temporally Specific Networks
190(3)
5.6 Important Subnetworks
193(9)
5.6.1 Line Islands
194(2)
5.6.2 Line Islands with Patents Tagged by Keywords
196(5)
5.6.3 Vertex Islands
201(1)
5.7 Citation Patterns
202(9)
5.7.1 Patents from 1976, Cited Through to 2006
204(5)
5.7.2 Patents from 1987, Cited Through to 2006
209(2)
5.8 Comparing Citation Patterns for Two Time Intervals
211(3)
5.9 Summary and Conclusions
214(2)
6 The US Supreme Court Citation Network
216(47)
6.1 Introduction
217(2)
6.2 Co-cited Islands of Supreme Court Decisions
219(3)
6.3 A Native American Line Island
222(6)
6.3.1 Forced Removal of Native American Populations
222(2)
6.3.2 Regulating Whites on Native American Lands
224(1)
6.3.3 Curtailing the Authority of Native American Courts
224(1)
6.3.4 Taxing Native Americans and Enforcing External Laws
225(1)
6.3.5 The Presence of Non-Native Americans on Native American Lands
226(1)
6.3.6 Some Later Developments
227(1)
6.3.7 A Partial Summary
227(1)
6.4 A `Perceived Threats to Social Order' Line Island
228(18)
6.4.1 Perceived Threats to Social Order
228(2)
6.4.2 The Structures of the Threats to Social Order Line Island
230(1)
6.4.3 Decisions Involving Communists and Socialists
230(6)
6.4.4 Restrictions of Labor Groups Organizing
236(1)
6.4.5 Restrictions of African Americans Organizing
237(2)
6.4.6 Jehovah's Witnesses as a Perceived Threat
239(4)
6.4.7 Obscenity as a Threat to Social Order
243(3)
6.5 Other Perceived Threats
246(4)
6.6 The Dred Scott Decision
250(11)
6.6.1 Citations from Dred Scott
251(2)
6.6.2 Citations to Dred Scott
253(7)
6.6.3 Methodological Implications of Dred Scott
260(1)
6.7 Further Reflections on the Supreme Court Citation Network
261(2)
7 Football as the World's Game
263(45)
7.1 A Brief Historical Overview
264(1)
7.2 Football Clubs
264(2)
7.3 Football Players
266(1)
7.4 Football in England
267(1)
7.5 Player Migrations
268(1)
7.6 Institutional Arrangements and the Organization of Football
269(2)
7.7 Court Rulings
271(1)
7.8 Specific Factors Impacting Football Migration
272(1)
7.9 Some Arguments and Propositions
272(6)
7.10 Some Preliminary Results
278(25)
7.10.1 The Non-English Presence in the EPL
279(10)
7.10.2 Player Fitness
289(3)
7.10.3 Starting Clubs for English Players
292(3)
7.10.4 General Features of the Top Five European Leagues
295(6)
7.10.5 Flows of Footballers into the Top European Leagues
301(2)
7.11 Player Ages When Recruited to the EPL
303(2)
7.12 A Partial Summary of Results
305(3)
8 Networks of Player Movements to the EPL
308(45)
8.1 Success in the EPL
308(3)
8.2 The Overall Presence of Other Countries in the EPL
311(1)
8.3 Network Flows of Footballers Between Clubs to Reach the EPL
312(6)
8.3.1 Moving Directly into the EPL from Local and Non-local Clubs
313(2)
8.3.2 Direct Moves of Players to the EPL from Non-EPL Clubs
315(3)
8.4 Moves from EPL Clubs
318(6)
8.4.1 The 1992--1996 Time Slice Flows with at Least Three Moves
318(4)
8.4.2 The 1997--2001 Time Slice Flows with at Least Three Moves
322(1)
8.4.3 The 2002--2006 Time Slice Flows with at Least Three Moves
323(1)
8.5 Moves Solely Within the EPL
324(6)
8.5.1 Loans
324(2)
8.5.2 Transfers
326(4)
8.6 All Trails of Footballers to the EPL
330(20)
8.6.1 Counted Features of Trails to the EPL
331(4)
8.6.2 Clustering Player Trails
335(15)
8.6.3 Interpreting the Clusters of Player Careers
350(1)
8.7 Summary and Conclusions
350(3)
9 Mapping Spatial Diversity in the United States of America
353(29)
9.1 Mapping Nations as Spatial Units of the United States
354(5)
9.1.1 The Counties of the United States
357(2)
9.2 Representing Networks in Space
359(1)
9.3 Clustering with a Relational Constraint
360(9)
9.3.1 Conditions for Hierarchical Clustering Methods
361(2)
9.3.2 Clustering with a Relational Constraint
363(2)
9.3.3 An Agglomerative Method for Relational Constraints
365(2)
9.3.4 Hierarchies
367(1)
9.3.5 Fast Agglomerative Clustering Algorithms
368(1)
9.4 Data for Constrained Spatial Clustering
369(5)
9.4.1 Discriminant Analysis for Garreau's Nations
369(5)
9.5 Clustering the US Counties with a Spatial Relational Constraint
374(7)
9.5.1 The Eight Garreau Nations in the USA
375(4)
9.5.2 The Ten Woodard Nations in the USA
379(2)
9.6 Summary
381(1)
10 On Studying Large Networks
382(13)
10.1 Substance
382(2)
10.2 Methods, Techniques, and Algorithms
384(1)
10.3 Network Data
385(3)
10.4 Surprises and Issues Triggered by Them
388(2)
10.5 Future Work
390(3)
10.6 Two Final Comments
393(2)
Appendix: Data Documentation
395(33)
A.1 Bibliographic Networks
395(5)
A.1.1 Centrality Literature Networks
397(2)
A.1.2 SNA Literature
399(1)
A.2 Patent Data
400(1)
A.3 Supreme Court Data
401(2)
A.4 Football Data
403(12)
A.4.1 Core Data
403(10)
A.4.2 Ancillary Data
413(2)
A.5 The USA Spatial County Network
415(13)
References
419(9)
Person Index 428(4)
Subject Index 432
Vladimir Batagelj, Department of Mathematics, Faculty of Mathematics and Physics, University of Ljubljana, Slovenia

Patrick Doreian, Faculty of Social Sciences, University of Ljubljana, Slovenia andDepartment of Sociology, University of Pittsburgh, USA

Anuka Ferligoj, Faculty of Social Sciences, University of Ljubljana, Slovenia

Nataa Kejar, Faculty of Medicine, Institute for Biostatistics and Medical Informatics, University of Ljubljana, Slovenia
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