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Table of Contents:

Chapter 1: Introduction
– Background of the Study
– Statement of the Problem
– Research Questions
– Significance of the Study
– Scope of Study
– Limitations of Study
– Objectives of Study

Chapter 2: Literature Review
– Introduction to Social Networks
– Topological Data Analysis
– Applications of TDA in Social Networks
– Previous Studies on TDA in Social Networks
– Gaps in Literature

Chapter 3: Research Methodology
– Research Design
– Data Collection Methods
– Data Analysis Techniques
– Sampling Methods
– Research Ethics

Chapter 4: Discussion of Findings
– Presentation of Data
– Data Analysis
– Interpretation of Results
– Comparison with Literature
– Implications of Findings

Chapter 5: Conclusion and Summary
– Summary of Findings
– Conclusion
– Recommendations for Future Research
– Practical Implications
– Contributions to the Field

Overview:

Topological data analysis (TDA) is a powerful mathematical tool that has gained popularity in recent years for its ability to analyze complex datasets, particularly in the field of social networks. This thesis aims to explore the application of TDA in social networks, specifically looking at how it can be used to extract meaningful insights and patterns from large-scale social network data.

The introduction will provide background information on social networks and TDA, highlighting the motivation for the study. The literature review will delve into existing research on TDA in social networks, discussing its potential applications and limitations. The research methodology will outline the design, data collection methods, and analysis techniques used in the study.

The discussion of findings will present the results of the data analysis, including any significant patterns or insights that were uncovered. The conclusion will summarize the key findings of the study and discuss their implications for future research and practical applications in the field.

Overall, this thesis aims to contribute to the growing body of knowledge on TDA in social networks and provide valuable insights for researchers and practitioners working in this field.

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