As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well. This book serves that purpose.
DescriptionSocial network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion.
The book features:
• Social network analysis from a computational perspective using python to show
the significance of fundamental facets of network theory and the various metrics
used to measure the social network.
• An understanding of network analysis and motivations to model phenomena
as networks.
• Real-world networks established with human-related data frequently display
social properties, i.e., patterns in the graph from which human behavioral patterns
can be analyzed and extracted.
• Exemplifies information cascades that spread through an underlying social
network to achieve widespread adoption.
• Networkanalysisthatoffersanappreciationmethodtohealthsystemsandservices
to illustrate, diagnose, and analyze networks in health systems.
• The social web has developed a significant social and interactive data source that
pays exceptional attention to social science and humanities research.
• The benefits of artificial intelligence enable social media platforms to meet an increasing number of users and yield the biggest marketplace, thus helping social networking analysis distribute better customer understanding and aiding marketers
to target the right customers.
Back to Top Table of ContentsPreface
1. Overview of Social Network Analysis and Different Graph File FormatsAbhishek B. and Sumit Hirve
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 AnalysisAgathiya Raja, Gavaskar Kanagaraj and Mohammad Gouse Galety
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 SetsArman Abouali Galehdari, Behnaz Moradi and Mohammad Gouse Galety
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 NetworksVasanthakumar G. U.
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 HealthcareSailee Bhambere
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 Sasmita Pani, Bibhuprasad Sahu, Jibitesh Mishra, Sachi Nandan Mohanty and Amrutanshu Panigrahi
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 AlgorithmsGouse Baig Mohammad, S. Shitharth and P. Dileep
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 MediaR. Vijaya Prakash
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 Madhuri Thimmapuram, Devasish Pal and Gouse Baig Mohammad
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 ModelsBithika Bishesh
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 SetsArulkumar N., Joy Paulose, Mohammad Gouse Galety, Manimaran A., S. Saravanan and Saleem Raja A.
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
IndexBack to Top