Social Networks: Modeling and Analysis

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