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Graph Neural Networks (GNNs) have gained significant attention in recent years for their ability to effectively model relational data. They are neural networks that operate on graph-structured data, allowing them to capture complex relationships and dependencies among entities. In this thesis, we explore the application of GNNs for relational data, with a focus on their potential in various domains such as social networks, recommendation systems, and bioinformatics.

Chapter One: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objectives of the Study
1.4 Limitations of the Study
1.5 Scope of the Study

Chapter Two: Literature Review
2.1 Overview of Graph Neural Networks
2.2 Applications of GNNs in Relational Data
2.3 Related Works in the Field
2.4 Current Challenges and Future Directions

Chapter Three: Research Methodology
3.1 Data Collection and Preprocessing
3.2 Model Architecture
3.3 Training and Evaluation
3.4 Performance Metrics

Chapter Four: Discussion of Findings
4.1 Experimental Results
4.2 Analysis and Interpretation of Results
4.3 Comparison with Existing Methods
4.4 Insights and Implications

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contribution to the Field
5.3 Future Research Directions
5.4 Conclusion

Thesis Overview:

Graph Neural Networks (GNNs) have emerged as a powerful paradigm for analyzing and modeling relational data. This thesis delves into the application of GNNs in various domains, with a particular focus on their ability to capture complex relationships and dependencies. The introductory chapter provides the background and rationale for the study, outlining the objectives, limitations, and scope of the research.

The literature review chapter surveys the current state of the art in GNNs for relational data, discussing their architecture, applications, and challenges. The research methodology chapter details the data collection and preprocessing steps, as well as the model architecture, training, and evaluation procedures.

The discussion of findings chapter presents the experimental results, analyzes and interprets the results, and compares them with existing methods. The conclusion and summary chapter summarizes the findings, highlights the contribution to the field, suggests future research directions, and concludes the thesis. Overall, this thesis aims to contribute to the growing body of research on GNNs for relational data, showcasing their potential in a variety of domains.

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