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Introduction:

Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing complex relational data, such as social networks. With the explosive growth of online social platforms, there is a growing need to understand the underlying structures and dynamics of social networks. GNNs offer a unique approach to modeling and analyzing these networks by capturing both the local and global information encoded in the graph structure.

This thesis aims to explore the application of GNNs for social network analysis, focusing on tasks such as node classification, link prediction, and community detection. By leveraging the expressive power of GNNs, we aim to improve the performance of traditional methods in capturing the intricate relationships within social networks.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of Graph Neural Networks
2.2 Social Network Analysis
2.3 Traditional Methods for Social Network Analysis
2.4 Applications of GNNs in Social Network Analysis
2.5 Node Classification in Social Networks
2.6 Link Prediction in Social Networks
2.7 Community Detection in Social Networks
2.8 Challenges and Limitations of GNNs in Social Network Analysis
2.9 Comparative Analysis of GNNs and Traditional Methods
2.10 Future Directions in GNNs for Social Network Analysis

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 GNN Model Selection
3.4 Training and Evaluation
3.5 Hyperparameter Tuning
3.6 Performance Metrics
3.7 Experiment Design
3.8 Statistical Analysis

Chapter 4: Discussion of Findings
4.1 Node Classification Results
4.2 Link Prediction Results
4.3 Community Detection Results
4.4 Comparison with Traditional Methods
4.5 Interpretation of Results
4.6 Insights and Implications
4.7 Limitations and Future Work
4.8 Recommendations for Practitioners
4.9 Ethical Considerations

Chapter 5: Conclusion and Summary
5.1 Recap of Objectives
5.2 Key Findings
5.3 Contributions to the Field
5.4 Implications for Social Network Analysis
5.5 Future Research Directions
5.6 Conclusion

Thesis Overview:
Social networks have become integral in our daily lives, allowing people to connect, share information, and form communities online. As the complexity and scale of social networks continue to increase, traditional methods for analyzing them fall short in capturing the intricate relationships and dynamics present within these networks. Graph Neural Networks (GNNs) offer a novel and powerful approach to addressing these challenges by leveraging the underlying graph structure of social networks.

The primary objective of this thesis is to explore the application of GNNs for social network analysis, with a focus on tasks such as node classification, link prediction, and community detection. By harnessing the expressive power of GNNs, we aim to improve the performance of traditional methods and provide new insights into the underlying structure and dynamics of social networks.

Chapter 1 provides an introduction to the field of social network analysis and the motivation behind using GNNs for this purpose. It outlines the research problem, objectives, scope, significance, and structure of the thesis. Additionally, key terms and concepts relevant to the study are defined to provide a solid foundation for the reader.

Chapter 2 presents a comprehensive literature review on Graph Neural Networks, social network analysis, traditional methods, applications of GNNs in social network analysis, and future directions in the field. This chapter lays the groundwork for the research methodology and helps identify gaps in the existing literature.

Chapter 3 details the research methodology, including data collection, preprocessing, GNN model selection, training and evaluation, hyperparameter tuning, performance metrics, experiment design, and statistical analysis. By following a rigorous methodology, we aim to ensure the validity and reliability of our results.

Chapter 4 discusses the findings of the study, including results from node classification, link prediction, and community detection tasks. A comparative analysis with traditional methods is provided, along with an interpretation of the results, insights, and implications for practitioners. Additionally, limitations and future research directions are discussed.

Chapter 5 concludes the thesis by summarizing the key findings, contributions to the field, implications for social network analysis, and future research directions. By leveraging the power of Graph Neural Networks, this thesis aims to advance the field of social network analysis and provide valuable insights into the structure and dynamics of social networks.

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