Context: Social networks serve as valuable platforms for studying human interactions and social dynamics. This project conducts an in-depth analysis of an educational system, focusing on the structure of friendships within our class and the dynamics of information propagation.

Objective: The primary objective is to introduce the fundamentals of network analysis, particularly emphasizing the basics of drawing a network diagram. This includes understanding the significance of network analysis, its applications, limitations, and terminologies, along with the rules and traditions associated with drawing network diagrams.

Materials/Methods : This study deals with a large and intricate dataset comprising 330 rows and 2 columns, representing connections between entities. Each edge is represented by a new row, with 'start name' and 'end name' columns. The dataset contains 328 edges and 102 nodes, illustrating a complex network structure that may be challenging to interpret. To simplify the analysis of complex social networks, the dataset was divided into smaller subsets, each containing 10 rows, to facilitate easier comprehension of relationships. By breaking down the dataset, users can navigate and interpret the social network more effectively.

Result : Two algorithms, namely the Random Layout Algorithm and the Spring Layout Algorithm, were employed to visualize the graph of the network. Additionally, Centrality Measure and Clustering Algorithms were utilized to identify correlations betweennetworks. After applying the Random Layout and Spring Layout Algorithms to visualize the network graph, it was observed that the division of the dataset into smaller subsets significantly enhanced the interpretability of the relationships between nodes. This result demonstrates the practical utility of the proposed method in facilitating effective navigation and interpretation of complex social networks, thereby contributing to enhanced connectivity and communication within educational environments.