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Introduction:

Graph Embedding Techniques for Social Network Analysis is a field of research that focuses on extracting meaningful representations of graph data in order to analyze and understand social networks. By transforming the complex graph structures into low-dimensional vector representations, researchers are able to apply a variety of machine learning algorithms to analyze the network properties and uncover hidden patterns within the data. This thesis aims to explore the different graph embedding techniques and their applications in social network analysis.

Table of Contents:

Chapter 1: Introduction
– Introduction
– Objective of study
– Limitation of study
– Scope of study

Chapter 2: Literature Review
– Overview of social network analysis
– Introduction to graph embedding techniques
– Types of graph embedding techniques
– Applications of graph embedding in social network analysis

Chapter 3: Research Methodology
– Data collection and preprocessing
– Selection of graph embedding techniques
– Evaluation metrics
– Experimental setup

Chapter 4: Discussion of Findings
– Analysis of experimental results
– Comparison of different graph embedding techniques
– Interpretation of findings

Chapter 5: Conclusion and Summary
– Summary of key findings
– Contributions to the field
– Future research directions

Thesis Overview:

Graph Embedding Techniques for Social Network Analysis is a comprehensive study that explores the various methods used to extract meaningful representations of graph data for the purpose of analyzing social networks. The thesis begins with an introduction to the field, highlighting the objectives, limitations, and scope of the study. The literature review covers the basics of social network analysis, introduces graph embedding techniques, and discusses the different types and applications of graph embeddings in social network analysis.

The research methodology section outlines the data collection and preprocessing steps, the selection of graph embedding techniques, the evaluation metrics used, and the experimental setup. The discussion of findings chapter analyzes the experimental results, compares different graph embedding techniques, and provides interpretations of the findings. The conclusion and summary chapter summarizes the key findings, discusses the contributions to the field, and suggests future research directions in the area of graph embedding techniques for social network analysis.

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