<![CDATA[Social Network Analysis (SNA) is a crucial quantitative methodology for mapping relationships and identifying connectivity structures within a group. This research specifically explores the use of the NetworkX library in Python as an effective tool for analyzing social networks. The primary objective of this study is to apply the Degree Centrality method to measure the level of connectivity and identify the most popular actors in a social network. The methodology employed is the quantitative analysis of an undirected graph modeled from the us_edgelist.csv dataset, which contains a list of relationships among political figures in an edge list format. Data processing utilized pandas, and the graph object was constructed using NetworkX. Degree Centrality was calculated for each node, with the results being normalized to provide a relative value. This normalization allows for a direct comparison of how active each actor is within the network. The centrality results were then visualized, with node sizes adjusted based on their Degree Centrality score. The results of the analysis indicate that figures like Bush and Obama possess the highest Degree Centrality score, 0.25, suggesting they have the greatest number of direct connections in this network. This high value confirms their role as the most active or central actors in the exchange and interaction within the political network studied. This finding validates the effectiveness of Degree Centrality as an indicator of high involvement. The study concludes that the implementation of Social Network Analysis using NetworkX provides a robust framework for understanding political relationship structures. Therefore, Degree Centrality is a reliable metric for quantifying actor activity and accurately identifying individuals who form the center of connections within the network.]]>
