This chapter presents a comprehensive approach to detecting and analyzing cloned profiles within social media platforms, focusing specifically on LinkedIn. The methodology integrates advanced techniques including a hybrid similarity detection model that combines the Levenshtein algorithm and sentence transformers for precise profile matching. By preprocessing the LinkedIn dataset and visualizing profiles in a graph database using Neo4j, the chapter introduces an innovative framework for detecting profile clones and assessing their impact on network integrity. The graph-based approach allows for efficient querying and visualization of profile relationships, providing a robust mechanism for real-time clone detection. The chapter also discusses with practical implications, including a user interface for visualizing clone detection results, ensuring that the developed system is both functional and user-friendly. The chapter highlights the importance of impact analysis of a clone too, which provides an insight into quantifying the damage of the impersonation.