Social Network Analysis : Theory and Applications

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Title Page
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Contents
Preface
1 Overview of Social Network Analysis and Different Graph File Formats
	1.1 Introduction—Social Network Analysis
	1.2 Important Tools for the Collection and Analysis of Online Network Data
	1.3 More on the Python Libraries and Associated Packages
	1.4 Execution of SNA in Terms of Real-Time Application: Implementation in Python
	1.5 Clarity Toward the Indices Employed in the Social Network Analysis
		1.5.1 Centrality
		1.5.2 Transitivity and Reciprocity
		1.5.3 Balance and Status
	1.6 Conclusion
	References
2 Introduction To Python for Social Network Analysis
	2.1 Introduction
	2.2 SNA and Graph Representation
		2.2.1 The Common Representation of Graphs
		2.2.2 Important Terms to Remember in Graph Representation
	2.3 Tools To Analyze Network
		2.3.1 MS Excel
		2.3.2 UCINET
	2.4 Importance of Analysis
	2.5 Scope of Python in SNA
		2.5.1 Comparison of Python With Traditional Tools
	2.6 Installation
		2.6.1 Good Practices
	2.7 Use Case
		2.7.1 Facebook Case Study
	2.8 Real-Time Product From SNA
		2.8.1 Nevaal Maps
	References
3 Handling Real-World Network Data Sets
	3.1 Introduction
	3.2 Aspects of the Network
	3.3 Graph
		3.3.1 Node, Edges, and Neighbors
		3.3.2 Small-World Phenomenon
	3.4 Scale-Free Network
	3.5 Network Data Sets
	3.6 Conclusion
	References
4 Cascading Behavior in Networks
	4.1 Introduction
		4.1.1 Types of Data Generated in OSNs
		4.1.2 Unstructured Data
		4.1.3 Tools for Structuring the Data
	4.2 User Behavior
		4.2.1 Profiling
		4.2.2 Pattern of User Behavior
		4.2.3 Geo-Tagging
	4.3 Cascaded Behavior
		4.3.1 Cross Network Behavior
		4.3.2 Pattern Analysis
		4.3.3 Models for Cascading Pattern
	References
5 Social Network Structure and Data Analysis in Healthcare
	5.1 Introduction
	5.2 Prognostic Analytics—Healthcare
	5.3 Role of Social Media for Healthcare Applications
	5.4 Social Media in Advanced Healthcare Support
	5.5 Social Media Analytics
		5.5.1 Phases Involved in Social Media Analytics
		5.5.2 Metrics of Social Media Analytics
		5.5.3 Evolution of NIHR
	5.6 Conventional Strategies in Data Mining Techniques
		5.6.1 Graph Theoretic
		5.6.2 Opinion Evaluation in Social Network
		5.6.3 Sentimental Analysis
	5.7 Research Gaps in the Current Scenario
	5.8 Conclusion and Challenges
	References
6 Pragmatic Analysis of Social Web Components on Semantic Web Mining
	6.1 Introduction
	6.2 Background
		6.2.1 Web
		6.2.2 Agriculture Information Systems
		6.2.3 Ontology in Web or Mobile Web
	6.3 Proposed Model
		6.3.1 Developing Domain Ontology
		6.3.2 Building the Agriculture Ontology with OWL-DL
			6.3.2.1 Building Class Axioms
		6.3.3 Building Object Property Between the Classes in OWL-DL
			6.3.3.1 Building Object Property Restriction in OWL-DL
		6.3.4 Developing Social Ontology
			6.3.4.1 Building Class Axioms
			6.3.4.2 Analysis of Social Web Components on Domain Ontology Under Agriculture System
	6.4 Building Social Ontology Under the Agriculture Domain
		6.4.1 Building Disjoint Class
		6.4.2 Building Object Property
	6.5 Validation
	6.6 Discussion
	6.7 Conclusion and Future Work
	References
7 Classification of Normal and Anomalous Activities in a Network by Cascading C4.5 Decision Tree and K-Means Clustering Algorithms
	7.1 Introduction
		7.1.1 Cascade Blogosphere Information
		7.1.2 Viral Marketing Cascades
		7.1.3 Cascade Network Building
		7.1.4 Cascading Behavior Empirical Research
		7.1.5 Cascades and Impact Nodes Detection
		7.1.6 Topologies of Cascade Networks
		7.1.7 Proposed Scheme Contributions
	7.2 Literature Survey
		7.2.1 Network Failures
	7.3 Methodology
		7.3.1 K-Means Clustering for Anomaly Detection
		7.3.2 C4.5 Decision Trees Anomaly Detection
	7.4 Implementation
		7.4.1 Training Phase Zi
		7.4.2 Testing Phase
	7.5 Results and Discussion
		7.5.1 Data Sets
		7.5.2 Experiment Evaluation
	7.6 Conclusion
	References
8 Machine Learning Approach To Forecast the Word in Social Media
	8.1 Introduction
	8.2 Related Works
	8.3 Methodology
		8.3.1 TF-IDF Technique
		8.3.2 Times Series
	8.4 Results and Discussion
	8.5 Conclusion
	References
9 Sentiment Analysis-Based Extraction of Real-Time Social Media Information From Twitter Using Natural Language Processing
	9.1 Introduction
		9.1.1 Applications for Social Media
		9.1.2 Social Media Data Challenges
	9.2 Literature Survey
		9.2.1 Techniques in Sentiment Analysis
	9.3 Implementation and Results
		9.3.1 Online Commerce
		9.3.2 Feature Extraction
		9.3.3 Hashtags
		9.3.4 Punctuations
	9.4 Conclusion
	9.5 Future Scope
	References
10 Cascading Behavior: Concept and Models
	10.1 Introduction
	10.2 Cascade Networks
	10.3 Importance of Cascades
	10.4 Purposes for Studying Cascades
	10.5 Collective Action
	10.6 Cascade Capacity
	10.7 Models of Network Cascades
		10.7.1 Decision-Based Diffusion Models
		10.7.2 Probabilistic Model of Cascade
		10.7.3 Linear Threshold Model
		10.7.4 Independent Cascade Model
		10.7.5 SIR Epidemic Model
	10.8 Centrality
	10.9 Cascading Failures
	10.10 Cascading Behavior Example Using Python
	10.11 Conclusion
	References
11 Exploring Social Networking Data Sets
	11.1 Introduction
		11.1.1 Network Theory
		11.1.2 Social Network Analysis
	11.2 Establishing a Social Network
		11.2.1 Designing the Symmetric Social Network
		11.2.2 Creating an Asymmetric Social Network
		11.2.3 Implementing and Visualizing Weighted
Social Networks
		11.2.4 Developing the Multigraph for Social Networks
	11.3 Connectivity of Users in Social Networks
		11.3.1 The Degree to which a Network Exists
		11.3.2 Coefficient of Clustering
		11.3.3 The Shortest Routes and Length Between Two Nodes
		11.3.4 Eccentricity Distribution of a Node
in a Social Network
		11.3.5 Scale-Independent Social Networks
		11.3.6 Transitivity
	11.4 Centrality Measures in Social Networks
		11.4.1 Centrality by Degree
		11.4.2 Centrality by Eigenvectors
		11.4.3 Centrality by Betweenness
		11.4.4 Closeness to All Other Nodes
	11.5 Case Study of Facebook
	11.6 Conclusion
	References
Index
EULA
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