Understanding large temporal networks and spatial networks : exploration, pattern searching, visualization and network evolution /

"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"--

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Batagelj, Vladimir, 1948-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken : Wiley, 2014.
Temas:
Social networks > Mathematical models.
Social networks > Computer simulation.
Réseaux sociaux > Modèles mathématiques.
Réseaux sociaux > Simulation par ordinateur.
MATHEMATICS > Probability & Statistics > General.
PSYCHOLOGY > Social Psychology.
Social networks > Mathematical models
Llibres electrònics.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: 1. Temporal and Spatial Networks
  • 1.1. Modern Social Network Analysis
  • 1.2.Network Sizes
  • 1.3. Substantive Concerns
  • 1.3.1. Citation Networks
  • 1.3.2. Other Types of Large Networks
  • 1.4.Computational Methods
  • 1.5. Data for Large Temporal Networks
  • 1.5.1. The Main Datasets
  • 1.5.2. Secondary Datasets
  • 1.6. Induction and Deduction
  • 2. Foundations of Methods for Large Networks
  • 2.1.Networks
  • 2.1.1. Descriptions of Networks
  • 2.1.2. Degrees
  • 2.1.3. Descriptions of Properties
  • 2.1.4. Visualizations of Properties
  • 2.2. Types of Networks
  • 2.2.1. Temporal Networks
  • 2.2.2. Multirelational Networks
  • 2.2.3. Two-mode Networks
  • 2.3. Large Networks
  • 2.3.1. Small and Middle Sized Networks
  • 2.3.2. Large Networks
  • 2.3.3.Complexity of Algorithms
  • 2.4. Strategies for Analyzing Large Networks
  • 2.5. Statistical Network Measures
  • 2.5.1. Using Pajek and R Together
  • 2.5.2. Fitting Distributions
  • 2.6. Subnetworks
  • 2.6.1. Clusters, Clusterings, Partitions, Hierarchies
  • 2.6.2. Contractions of Clusters
  • 2.6.3. Subgraphs
  • 2.6.4. Cuts
  • 2.7. Connectivity Properties of Networks
  • 2.7.1. Walks
  • 2.7.2. Equivalence Relations and Partitions
  • 2.7.3. Connectivity
  • 2.7.4. Condensation
  • 2.7.5. Bow-tie Structure of the Web Graph
  • 2.7.6. The Internal Structure of Strong Components
  • 2.7.7. Bi-connectivity and k-connectivity
  • 2.8. Triangular and Short Cycle Connectivities
  • 2.9. Islands
  • 2.9.1. Defining Islands
  • 2.9.2. Some Properties of Islands
  • 2.10. Cores and Generalized Cores
  • 2.10.1. Cores
  • 2.10.2. Generalized Cores
  • 2.11. Important Vertices in Networks
  • 2.11.1. Degrees, Closeness, Betweenness and Other Indices
  • 2.11.2. Clustering
  • 2.11.3.Computing Further Indices Through Functions
  • 2.12. Transition to Methods for Large Networks
  • 3. Methods for Large Networks
  • 3.1. Acyclic Networks
  • 3.1.1. Some Basic Properties of Acyclic Networks
  • 3.1.2.Compatible Numberings: Depth and Topological Order
  • 3.1.3. Topological Orderings and Functions on Acyclic Networks
  • 3.2. SPC Weights in Acyclic Networks
  • 3.2.1. Citation Networks
  • 3.2.2. Analysis of Citation Networks
  • 3.2.3. Search Path Count Method
  • 3.2.4.Computing SPLC and SPNP Weights
  • 3.2.5. Implementation Details
  • 3.2.6. Vertex Weights
  • 3.2.7. General Properties of Weights
  • 3.2.8. SPC Weights
  • 3.3. Probabilistic Flow in Acyclic Network
  • 3.4. Nonacyclic Citation Networks
  • 3.5. Two-mode Networks from Data Tables
  • 3.5.1. Multiplication of Two-mode Networks
  • 3.6. Bibliographic Networks
  • 3.6.1. Co-authorship Networks
  • 3.6.2. Collaboration Networks
  • 3.6.3. Other Derived Networks
  • 3.7. Weights
  • 3.7.1. Normalizations of Weights
  • 3.7.2.k-Rings
  • 3.7.3.4-Rings and Analysis of Two-mode Networks
  • 3.7.4. Two-mode Cores
  • 3.8. Pathfinder
  • 3.8.1. Pathfinder Algorithms
  • 3.8.2.Computing the Closure Over the Pathfinder Semiring
  • 3.8.3. Spanish Algorithms
  • 3.8.4.A Sparse Network Algorithm
  • 3.9. Clustering, Blockmodeling, and Community Detection
  • 3.9.1. The Louvain Method and VOS
  • 3.10. Clustering Symbolic Data
  • 3.10.1. Symbolic Objects Described with Distributions
  • 3.10.2. The Leaders Method
  • 3.10.3. An Agglomerative Method
  • 3.11. Approaches to Temporal Networks
  • 3.11.1. Journeys
  • Walks in Temporal Networks
  • 3.11.2. Measures
  • 3.11.3. Problems and Algorithms
  • 3.11.4. Evolution
  • 3.12. Levels of Analysis
  • 3.13. Transition to Substantive Topics
  • 4. Scientific Citation and Other Bibliographic Networks
  • 4.1. The Centrality Citation Network
  • 4.2. Preliminary Data Analyses
  • 4.2.1. Temporal Distribution of Publications
  • 4.2.2. Degree Distributions of the Centrality Literature
  • 4.2.3. Types of Works
  • 4.2.4. The Boundary Problem
  • 4.3. Transforming a Citation Network into an Acyclic Network
  • 4.3.1. Checking for the Presence of Cycles
  • 4.3.2. Dealing with Cycles in Citation Networks
  • 4.4. The Most Important Works
  • 4.5. SPC Weights
  • 4.5.1. Obtaining SPC Weights and Drawing Main Paths
  • 4.5.2. The Main Path of the Centrality Citation Network
  • 4.6. Line Cuts
  • 4.7. Line Islands
  • 4.7.1. The Main Island
  • 4.7.2.A Geophysics and Meteorology Line Island
  • 4.7.3. An Optical Network Line Island
  • 4.7.4.A Partial Summary of Main Path and Line Island Results
  • 4.8. Other Relevant Subnetworks for a Bounded Network
  • 4.9. Collaboration Networks
  • 4.9.1. Macros for Collaboration Networks
  • 4.9.2. An Initial Attempt of Analyses of Collaboration Networks
  • 4.10.A Brief Look at the SNA Literature SN5 Networks
  • 4.11. On the Centrality and SNA Collaboration Networks
  • References
  • 5. Citation Patterns in Temporal United States Patent Data
  • 5.1. Patents
  • 5.2. Supreme Court Decisions Regarding Patents
  • 5.2.1. Co-cited Decisions
  • 5.2.2. Citations Between Co-cited Decisions
  • 5.3. The 1976
  • 2006 Patent Data
  • 5.4. Structural Variables Through Time
  • 5.4.1. Temporally Specific Networks
  • 5.4.2. Shrinking Specific Patent Citation Networks
  • 5.4.3. Structural Properties
  • 5.5. Some Patterns of Technological Development
  • 5.5.1. Structural Properties of Temporally Specific Networks
  • 5.6. Important Subnetworks
  • 5.6.1. Line Islands
  • 5.6.2. Line Islands with Patents Tagged by Keywords
  • 5.6.3. Vertex Islands
  • 5.7. Citation Patterns
  • 5.7.1. Patents from 1976, Cited Through to 2006
  • 5.7.2. Patents from 1987, Cited Through to 2006
  • 5.8.Comparing Citation Patterns for Two Time Intervals
  • 5.9. Summary and Conclusions
  • 6. The US Supreme Court Citation Network
  • 6.1. Introduction
  • 6.2. Co-cited Islands of Supreme Court Decisions
  • 6.3.A Native American Line Island
  • 6.3.1. Forced Removal of Native American Populations
  • 6.3.2. Regulating Whites on Native American Lands
  • 6.3.3. Curtailing the Authority of Native American Courts
  • 6.3.4. Taxing Native Americans and Enforcing External Laws
  • 6.3.5. The Presence of Non-Native Americans on Native American Lands
  • 6.3.6. Some Later Developments
  • 6.3.7.A Partial Summary
  • 6.4.A `Perceived Threats to Social Order' Line Island
  • 6.4.1. Perceived Threats to Social Order
  • 6.4.2. The Structures of the Threats to Social Order Line Island
  • 6.4.3. Decisions Involving Communists and Socialists
  • 6.4.4. Restrictions of Labor Groups Organizing
  • 6.4.5. Restrictions of African Americans Organizing
  • 6.4.6. Jehovah's Witnesses as a Perceived Threat
  • 6.4.7. Obscenity as a Threat to Social Order
  • 6.5. Other Perceived Threats
  • 6.6. The Dred Scott Decision
  • 6.6.1. Citations from Dred Scott
  • 6.6.2. Citations to Dred Scott
  • 6.6.3. Methodological Implications of Dred Scott
  • 6.7. Further Reflections on the Supreme Court Citation Network
  • 7. Football as the World's Game
  • 7.1.A Brief Historical Overview
  • 7.2. Football Clubs
  • 7.3. Football Players
  • 7.4. Football in England
  • 7.5. Player Migrations
  • 7.6. Institutional Arrangements and the Organization of Football
  • 7.7. Court Rulings
  • 7.8. Specific Factors Impacting Football Migration
  • 7.9. Some Arguments and Propositions
  • 7.10. Some Preliminary Results
  • 7.10.1. The Non-English Presence in the EPL
  • 7.10.2. Player Fitness
  • 7.10.3. Starting Clubs for English Players
  • 7.10.4. General Features of the Top Five European Leagues
  • 7.10.5. Flows of Footballers into the Top European Leagues
  • 7.11. Player Ages When Recruited to the EPL
  • 7.12.A Partial Summary of Results
  • 8.Networks of Player Movements to the EPL
  • 8.1. Success in the EPL
  • 8.2. The Overall Presence of Other Countries in the EPL
  • 8.3.Network Flows of Footballers Between Clubs to Reach the EPL
  • 8.3.1. Moving Directly into the EPL from Local and Non-local Clubs
  • 8.3.2. Direct Moves of Players to the EPL from Non-EPL Clubs
  • 8.4. Moves from EPL Clubs
  • 8.4.1. The 1992
  • 1996 Time Slice Flows with at Least Three Moves
  • 8.4.2. The 1997
  • 2001 Time Slice Flows with at Least Three Moves
  • 8.4.3.
  • The 2002
  • 2006 Time Slice Flows with at Least Three Moves
  • 8.5. Moves Solely Within the EPL
  • 8.5.1. Loans
  • 8.5.2. Transfers
  • 8.6. All Trails of Footballers to the EPL
  • 8.6.1. Counted Features of Trails to the EPL
  • 8.6.2. Clustering Player Trails
  • 8.6.3. Interpreting the Clusters of Player Careers
  • 8.7. Summary and Conclusions
  • 9. Mapping Spatial Diversity in the United States of America
  • 9.1. Mapping Nations as Spatial Units of the United States
  • 9.1.1. The Counties of the United States
  • 9.2. Representing Networks in Space
  • 9.3. Clustering with a Relational Constraint
  • 9.3.1. Conditions for Hierarchical Clustering Methods
  • 9.3.2. Clustering with a Relational Constraint
  • 9.3.3. An Agglomerative Method for Relational Constraints
  • 9.3.4. Hierarchies
  • 9.3.5. Fast Agglomerative Clustering Algorithms
  • 9.4. Data for Constrained Spatial Clustering
  • 9.4.1. Discriminant Analysis for Garreau's Nations
  • 9.5. Clustering the US Counties with a Spatial Relational Constraint
  • 9.5.1. The Eight Garreau Nations in the USA
  • 9.5.2. The Ten Woodard Nations in the USA
  • 9.6. Summary
  • 10. On Studying Large Networks
  • 10.1. Substance
  • 10.2. Methods, Techniques, and Algorithms
  • 10.3.Network Data
  • 10.4. Surprises and Issues Triggered by Them
  • 10.5. Future Work
  • 10.6. Two Final Comments
  • Appendix: Data Documentation
  • A.1. Bibliographic Networks
  • A.1.1. Centrality Literature Networks
  • A.1.2. SNA Literature
  • A.2. Patent Data
  • A.3. Supreme Court Data
  • A.4. Football Data
  • A.4.1. Core Data
  • A.4.2. Ancillary Data
  • A.5. The USA Spatial County Network
  • References.

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