| Description |
1 online resource |
| Series |
Advanced information and knowledge processing, 1610-3947 |
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Advanced information and knowledge processing.
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| Contents |
Preface; New Age of€Web Usage; Learn, in Simple Words, Theory and Practice of Social Network Analysis; Contents; Chapter 1: Theoretical Concepts of€Network Analysis; 1.1 Sociological Meaning of€Network Relations; 1.2 Network Measurements; 1.2.1 Network Connection; 1.2.2 Transitivity; 1.2.3 Multiplexity; 1.2.4 Homophily; 1.2.5 Dyads and€Mutuality; 1.2.6 Balance and€Triads; 1.2.7 Reciprocity; 1.3 Network Distribution; 1.3.1 Distance Between Two Nodes; 1.3.2 Degree Centrality; 1.3.3 Closeness Centrality; 1.3.4 Betweenness Centrality; 1.3.5 Eigenvector Centrality; 1.3.6 PageRank |
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1.3.7 Geodesic Distance and€Shortest Path1.3.8 Eccentricity; 1.3.9 Density; 1.4 Network Segmentation; 1.4.1 Cohesive Subgroups; 1.4.2 Cliques; 1.4.3 K-Cores; 1.4.4 Clustering Coefficient; 1.4.5 Core/Periphery; 1.4.6 Blockmodels; 1.4.7 Hierarchical Clustering; 1.5 Recent Developments in€Network Analysis; 1.5.1 Community Detection; 1.5.2 Link Prediction; 1.5.3 Spatial Networks; 1.5.4 Protein-Protein Interaction Networks; 1.5.5 Recommendation Systems; 1.6 iGraph; Chapter 2: Network Basics; 2.1 What Is a€Network?; 2.2 Types of€Networks; 2.3 Properties of€Networks; 2.4 Network Measures |
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2.5 NetworkX2.6 Installation; 2.7 Matrices; 2.8 Types of€Matrices in€Social Networks; 2.8.1 Adjacency Matrix; 2.8.2 Edge List Matrix; 2.8.3 Adjacency List; 2.8.4 Numpy Matrix; 2.8.5 Sparse Matrix; 2.9 Basic Matrix Operations; 2.10 Data Visualization; Chapter 3: Graph Theory; 3.1 Origins of€Graph Theory; 3.2 Graph Basics; 3.3 Vertices; 3.4 Types of€Graphs; 3.5 Graph Traversals; 3.5.1 Depth-First Traversal (DFS); 3.5.2 Breadth-First Traversal (BFS); 3.5.3 Dijkstra's Algorithm; 3.6 Operations on€Graphs; Reference; Chapter 4: Social Networks; 4.1 Social Networks |
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4.2 Properties of€a€Social Network4.2.1 Scale-Free Networks; 4.2.2 Small-World Networks; 4.2.3 Network Navigation; 4.2.4 Dunbar's Number; 4.3 Data Collection in€Social Networks; 4.4 Six Degrees of€Separation; 4.5 Online Social Networks; 4.6 Online Social Data Collection; 4.7 Data Sampling; 4.8 Social Network Analysis; 4.9 Social Network Analysis vs. Link Analysis; 4.10 Historical Development; 4.11 Importance of€Social Network Analysis; 4.12 Social Network Analysis Modeling Tools; References; Chapter 5: Node-Level Analysis; 5.1 Ego-Network Analysis |
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5.2 Identifying Influential Individuals in€the€Network5.2.1 Degree Centrality; 5.2.2 Closeness Centrality; 5.2.3 Betweenness Centrality; 5.2.4 Eigenvector Centrality; 5.3 PageRank; 5.4 Neighbors; 5.5 Bridges; 5.6 Which Centrality Algorithm to€Use?; Chapter 6: Group-Level Analysis; 6.1 Cohesive Subgroups; 6.2 Cliques; 6.3 Clustering Coefficient; 6.4 Triadic Analysis; 6.5 Structural Holes; 6.6 Brokerage; 6.7 Transitivity; 6.8 Coreness; 6.9 Overlapping Communities; 6.10 Dynamic Community Finding; 6.11 M-Slice; 6.12 K-Cores; 6.13 Community Detection; 6.13.1 Graph Partitioning |
| Summary |
This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities. Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed April 4, 2017) |
| Subject |
Python (Computer program language)
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Graph theory -- Data processing.
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Quantitative research.
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Online social networks -- Data processing
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Society & social sciences.
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Information retrieval.
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Programming & scripting languages: general.
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Systems analysis & design.
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COMPUTERS -- Web -- Social Media.
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Graph theory -- Data processing
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Python (Computer program language)
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Quantitative research
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| Form |
Electronic book
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| Author |
Kadry, Seifedine, 1977- author
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| ISBN |
9783319530048 |
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3319530046 |
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