Fundamentals of big data : network analysis for research and industry

Fundamentals of big data network analysis for research and industry

Detalles Bibliográficos
Otros Autores: Lee, Hyunjoung, author (author), Sohn, Il, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Chichester, UK : John Wiley & Sons 2016.
Edición:First edition
Colección:THEi Wiley ebooks.
Materias:
Data mining.
Big data.
Decision making > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849087406719
Tabla de Contenidos:
  • Intro
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • About the Authors
  • List of Figures
  • List of Tables
  • Chapter 1 Why Big Data?
  • 1.1 Big Data
  • 1.2 What Creates Big Data?
  • 1.3 How Do We Use Big Data?
  • 1.4 Essential Issues Related to Big Data
  • References
  • Chapter 2 Basic Programs for Analyzing Networks
  • 2.1 UCINET
  • 2.2 NetMiner
  • 2.3 R
  • 2.4 Gephi
  • 2.5 NodeXL
  • References
  • Chapter 3 Understanding Network Analysis
  • 3.1 Defining Social Network Analysis
  • 3.2 Basic SNA Concepts
  • 3.2.1 Basic Terminology
  • 3.2.2 Representation of a Network
  • 3.3 Social Network Data
  • 3.3.1 One-Mode and Two-Mode Networks
  • 3.3.2 Attributes and Weights
  • 3.3.3 Network Data Form
  • References
  • Chapter 4 Research Methods Using SNA
  • 4.1 SNA Research Procedures
  • 4.2 Identifying the Research Problem and Developing Hypotheses
  • 4.2.1 Identifying the Research Problem
  • 4.2.2 Developing Hypotheses
  • 4.3 Research Design
  • 4.3.1 Defining the Network Model
  • 4.3.2 Establishing Network Boundaries
  • 4.3.3 Measurement Evaluation
  • 4.4 Acquisition of Network Data
  • 4.4.1 Survey
  • 4.4.2 Interview, Observation, and Experiment
  • 4.4.3 Existing Data
  • 4.5 Data Cleansing
  • 4.5.1 Extraction of the Node and Link
  • 4.5.2 Merging and Separation of Data
  • 4.5.3 Directional Transformation in the Link
  • 4.5.4 Transformation of the Weights in Links
  • 4.5.5 Transformation of the Two-Mode Network to a One-Mode Network
  • References
  • Chapter 5 Position and Structure
  • 5.1 Position
  • 5.1.1 Degree Centrality
  • 5.1.2 Closeness Centrality
  • 5.1.3 Betweenness Centrality
  • 5.1.4 Prestige Centrality
  • 5.1.5 Broker
  • 5.2 Cohesive Subgroup
  • 5.2.1 Component
  • 5.2.2 Community
  • 5.2.3 Clique
  • 5.2.4 k-Core
  • References
  • Chapter 6 Connectivity and Role
  • 6.1 Connection Analysis
  • 6.1.1 Connectivity
  • 6.1.2 Reciprocity.
  • 6.1.3 Transitivity
  • 6.1.4 Assortativity
  • 6.1.5 Network Properties
  • 6.2 Role
  • 6.2.1 Structural Equivalence
  • 6.2.2 Automorphic Equivalence
  • 6.2.3 Role Equivalence
  • 6.2.4 Regular Equivalence
  • 6.2.5 Block Modeling
  • References
  • Chapter 7 Data Structure in NetMiner
  • 7.1 Sample Data
  • 7.1.1 01.Org_Net_Tiny1
  • 7.1.2 02.Org_Net_Tiny2
  • 7.1.3 03.Org_Net_Tiny3
  • 7.2 Main Concept
  • 7.2.1 Data Structure
  • 7.2.2 Creating Data
  • 7.2.3 Inserting Data
  • 7.2.4 Importing Data
  • 7.3 Data Preprocessing
  • 7.3.1 Change of Link
  • 7.3.2 Extraction and Reordering of the Node and Link
  • 7.3.3 Data Merge and Split
  • Reference
  • Chapter 8 Network Analysis Using NetMiner
  • 8.1 Centrality and Cohesive Subgroup
  • 8.1.1 Centrality
  • 8.1.2 Cohesive Subgroup
  • 8.2 Connectivity and Equivalence
  • 8.2.1 Connectivity
  • 8.2.2 Equivalence
  • 8.3 Visualization and Exploratory Analysis
  • 8.3.1 Visualization
  • 8.3.2 Transformation of the Two-Mode Network to a One-Mode Network
  • Appendix A Visualization
  • A.1 Spring Algorithm
  • A.2 Multidimensional Scaling Algorithm
  • A.3 Cluster Algorithm
  • A.4 Layered Algorithm
  • A.5 Circular Algorithm
  • A.6 Simple Algorithm
  • References
  • Appendix B Case Study: Knowledge Structure of Steel Research
  • Reference
  • Index
  • EULA.

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