Читать книгу Social Network Analysis - Группа авторов - Страница 2
Table of Contents
Оглавление1 Cover
4 Preface
5 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 1.6 Conclusion References
6 2 Introduction To Python for Social Network Analysis 2.1 Introduction 2.2 SNA and Graph Representation 2.3 Tools To Analyze Network 2.4 Importance of Analysis 2.5 Scope of Python in SNA 2.6 Installation 2.7 Use Case 2.8 Real-Time Product From SNA References
7 3 Handling Real-World Network Data Sets 3.1 Introduction 3.2 Aspects of the Network 3.3 Graph 3.4 Scale-Free Network 3.5 Network Data Sets 3.6 Conclusion References
8 4 Cascading Behavior in Networks 4.1 Introduction 4.2 User Behavior 4.3 Cascaded Behavior References
9 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 5.6 Conventional Strategies in Data Mining Techniques 5.7 Research Gaps in the Current Scenario 5.8 Conclusion and Challenges References
10 6 Pragmatic Analysis of Social Web Components on Semantic Web Mining 6.1 Introduction 6.2 Background 6.3 Proposed Model 6.4 Building Social Ontology Under the Agriculture Domain 6.5 Validation 6.6 Discussion 6.7 Conclusion and Future Work References
11 7 Classification of Normal and Anomalous Activities in a Network by Cascading C4.5 Decision Tree and K-Means Clustering Algorithms 7.1 Introduction 7.2 Literature Survey 7.3 Methodology 7.4 Implementation 7.5 Results and Discussion 7.6 Conclusion References
12 8 Machine Learning Approach To Forecast the Word in Social Media 8.1 Introduction 8.2 Related Works 8.3 Methodology 8.4 Results and Discussion 8.5 Conclusion References
13 9 Sentiment Analysis-Based Extraction of Real-Time Social Media Information From Twitter Using Natural Language Processing 9.1 Introduction 9.2 Literature Survey 9.3 Implementation and Results 9.4 Conclusion 9.5 Future Scope References
14 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 10.8 Centrality 10.9 Cascading Failures 10.10 Cascading Behavior Example Using Python 10.11 Conclusion References
15 11 Exploring Social Networking Data Sets 11.1 Introduction 11.2 Establishing a Social Network 11.3 Connectivity of Users in Social Networks 11.4 Centrality Measures in Social Networks 11.5 Case Study of Facebook 11.6 Conclusion References
16 Index
