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Table of Contents

Chapter 1: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Research Questions
1.4 Objectives of the Study
1.5 Significance of the Study
1.6 Limitations of the Study
1.7 Scope of the Study

Chapter 2: Literature Review
2.1 Overview of Graph Neural Networks
2.2 Social Network Analysis
2.3 Applications of Graph Neural Networks in Social Network Analysis
2.4 Existing Research on Graph Neural Networks for Social Network Analysis

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Social Network Data
4.2 Implementation of Graph Neural Networks
4.3 Evaluation of Results
4.4 Comparison with Existing Methods

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Recommendations for Future Research
5.4 Conclusion

Brief Overview:

The thesis on Graph Neural Networks for Social Network Analysis delves into the application of advanced machine learning techniques in analyzing social networks. The study aims to explore the use of Graph Neural Networks (GNNs) to extract meaningful insights from complex social network data.

Chapter 1 provides an introduction to the research, outlining the background, problem statement, research questions, objectives, significance, limitations, and scope of the study. Chapter 2 conducts a comprehensive literature review on GNNs, social network analysis, and their applications in the field.

Chapter 3 details the research methodology, including the research design, data collection methods, analysis techniques, and ethical considerations. Chapter 4 presents the findings of the study, discussing the analysis of social network data, implementation of GNNs, evaluation of results, and comparison with existing methods.

Chapter 5 concludes the thesis, summarizing the findings, discussing contributions to the field, providing recommendations for future research, and concluding the study. The thesis aims to contribute to the advancement of social network analysis using cutting-edge machine learning technologies.

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