Social Networks

Social Networks

by Niyati Aggrawal, Adarsh Anand
Released February 2022
Publisher(s): CRC Press
ISBN: 9781000540000

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Book description

The goal of this book is to provide a reference for applications of mathematical modelling in social media and related network analysis and offer a theoretically sound background with adequate suggestions for better decision-making.

Table of contents

  1. Cover Page
  2. Half Title Page
  3. Title Page
  4. Copyright Page
  5. Dedication Page
  6. Contents
  7. Preface
  8. Acknowledgements
  9. Authors
  10. Chapter 1 Introduction to Social Networks
    1. 1.1 Concept of Complex Networks
    2. 1.2 Overview of Social Network Analysis
      1. 1.2.1 Social Networks and Social Networking
      2. 1.2.2 Social Network Visualization and Statistical Analysis
      3. 1.2.3 Social Network Modelling
      4. 1.2.4 Link Prediction
      5. 1.2.5 Community Detection
      6. 1.2.6 Ego Network
      7. 1.2.7 Network Motifs
      8. 1.2.8 Security and Privacy Issues
    3. 1.3 Social Media Content
      1. 1.3.1 Content Characteristics
      2. 1.3.2 Content Dynamics
      3. 1.3.3 User Characteristics
    4. 1.4 Levels of Network Analysis
      1. 1.4.1 Micro-Level
      2. 1.4.2 Meso-Level
      3. 1.4.3 Macro-Level
    5. 1.5 Complex Networks
    6. 1.6 Problems for Self-Assessment
    7. References
  11. Chapter 2 Network Statistics and Related Concepts
    1. 2.1 Networks and Graphs
    2. 2.2 Different Types of Networks
      1. 2.2.1 Undirected Networks
      2. 2.2.2 Directed Networks
      3. 2.2.3 Self-Loops
      4. 2.2.4 Multigraph/Simple Graphs
      5. 2.2.5 Weighted Network
      6. 2.2.6 Complete Graph (Clique)
      7. 2.2.7 Bipartite Graph
    3. 2.3 Representation of the Networks
      1. 2.3.1 Adjacency Matrix
      2. 2.3.2 Real Networks are Sparse
      3. 2.3.3 Complete Graph
    4. 2.4 Network Properties
      1. 2.4.1 Node Degree
      2. 2.4.2 Average Degree
      3. 2.4.3 Degree Distribution
      4. 2.4.4 Paths and Distance in Graph
      5. 2.4.5 Shortest Path
      6. 2.4.6 Network Diameter
      7. 2.4.7 Average Path Length
      8. 2.4.8 Clustering Coefficient
    5. 2.5 Problems for Self-Assessment
    6. References
  12. Chapter 3 Network Models
    1. 3.1 Basic Features of Networks
      1. 3.1.1 Continuous Distribution
      2. 3.1.2 Discrete Distribution
    2. 3.2 Generative Models
      1. 3.2.1 Random Graph Models
      2. 3.2.2 Preferential Attachment Model
      3. 3.2.3 Small-World Model
    3. 3.3 Six Degrees of Separation
    4. 3.4 Problems for Self-Assessment
    5. References
  13. Chapter 4 Network Centrality
    1. 4.1 Centrality Measures Overview
    2. 4.2 Degree Centrality
    3. 4.3 Eigenvector Centrality
    4. 4.4 Katz Centrality
    5. 4.5 Betweenness Centrality
    6. 4.6 Closeness Centrality
    7. 4.7 Problems for Self-Assessment
    8. References
  14. Chapter 5 Link Analysis
    1. 5.1 Link Analysis in Web Mining
    2. 5.2 Ranking Algorithms
    3. 5.3 Hyperlink-Induced Topic Search (HITS)
    4. 5.4 Pagerank Algorithm
    5. 5.5 Problems for Self-Assessment
    6. References
  15. Chapter 6 Link Prediction
    1. 6.1 Overview of Link Prediction
    2. 6.2 Link Prediction Methods
      1. 6.2.1 Graph Distance
      2. 6.2.2 Common Neighbours
      3. 6.2.3 Jaccard's Coefficient
      4. 6.2.4 Adamic/Adar (Frequency-Weighted Common Neighbours)
      5. 6.2.5 Preferential Attachment
      6. 6.2.6 Katz (Exponentially Damped Path Counts)
      7. 6.2.7 Hitting Time
      8. 6.2.8 Rooted (Personalized) PageRank
    3. 6.3 Other Metrics
      1. 6.3.1 Friends Measure
      2. 6.3.2 Cosine Similarity
      3. 6.3.3 Sørensen Index
      4. 6.3.4 Hub Promoted Index
      5. 6.3.5 Hub Depressed Index
      6. 6.3.6 Leicht–Holme–Newman Index
    4. 6.4 Prediction Performance Metrics
    5. 6.5 Problems for Self-Assessment
    6. References
  16. Chapter 7 Community Detection
    1. 7.1 Overview of Community
    2. 7.2 Taxonomy of Community Criteria
      1. 7.2.1 Node-Centric Community Detection
      2. 7.2.2 Group-Centric Community Detection
      3. 7.2.3 Network-Centric Community Detection
      4. 7.2.4 Hierarchy-Centric Community Detection
    3. 7.3 Community Evaluation
    4. 7.4 Problems for Self-Assessment
    5. References
  17. Chapter 8 Ego Networks
    1. 8.1 Overview of Ego Networks
    2. 8.2 Characteristics of Ego Networks
    3. 8.3 Ego Network Measures
      1. 8.3.1 Ego Network Density
      2. 8.3.2 Structural Holes
      3. 8.3.3 Brokerage
    4. 8.4 Problems for Self-Assessment
    5. References
  18. Chapter 9 Network Cohesion
    1. 9.1 Overview of Network Cohesion
    2. 9.2 Triadic Closure
    3. 9.3 Embeddedness
    4. 9.4 Density
    5. 9.5 Dyadic Relation
    6. 9.6 Reciprocity
    7. 9.7 Homophily
    8. 9.8 Transitivity
    9. 9.9 Bridges
    10. 9.10 Group-External and Group-Internal Ties
    11. 9.11 Krackhardt's Graph Theoretical Dimensions of Hierarchy
    12. 9.12 Positions and Roles
    13. 9.13 Problems for Self-Assessment
    14. References
  19. Chapter 10 Information Diffusion
    1. 10.1 Overview of Information Diffusion
    2. 10.2 Explicit Networks
      1. 10.2.1 Herd Behaviour
      2. 10.2.2 Information Cascades
    3. 10.3 Implicit Networks
      1. 10.3.1 Diffusion of Innovations
      2. 10.3.2 Epidemical Models
    4. 10.4 Problems for Self-Assessment
    5. References
  20. Chapter 11 Security and Privacy in Social Networks
    1. 11.1 Introduction
    2. 11.2 Need of Privacy
    3. 11.3 Social Network Privacy Model
    4. 11.4 Basic Concepts in Data Privacy
      1. 11.4.1 K-Anonymity
      2. 11.4.2 L-Diversity
      3. 11.4.3 T-Closeness
    5. 11.5 Randomization
    6. 11.6 Slicing
    7. 11.7 Problems for Self-Assessment
    8. References
  21. Chapter 12 Social Network Analysis Tools
    1. 12.1 Overview of Social Network Analysis Tools
    2. 12.2 Various Tools
      1. 12.2.1 Gephi (Visualization and Basic Network Metrics)
      2. 12.2.2 NetLogo (Modelling Network Dynamics)
      3. 12.2.3 Igraph (for Programming Assignment)
      4. 12.2.4 Pajek (User Friendly, Free, Windows Only)
      5. 12.2.5 UCINET (Extensive, Socially Focused Functionality, Windows Only)
      6. 12.2.6 Network Overview Discovery Exploration for Excel (NodeXL) (SNA Integrated to Excel, Windows Only, Free, Beta)
      7. 12.2.7 NetMiner 4
      8. 12.2.8 NetworkX (Extensive Functionality, Scales to Large Networks by Taking Advantage of Existing C, Fortran Libraries for Large Matrix Computations, Open Source)
      9. 12.2.9 R (Extensive, Statistics-Heavy Functionality)
      10. 12.2.10 SocioViz
      11. 12.2.11 UNISoN (Social Network Analysis Tool)
      12. 12.2.12 Wolfram Alpha
    3. 12.3 Problems for Self-Assessment
    4. References
  22. Index

Product information

  • Title: Social Networks
  • Author(s): Niyati Aggrawal, Adarsh Anand
  • Release date: February 2022
  • Publisher(s): CRC Press
  • ISBN: 9781000540000