Social Network Analysis

Description

Social Network Analysis (2022) [Galety et al] [9781119836230] Cover Half-Title Page Series Page Title Page Copyright Page 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 Blank Blank