Newman and Girvan propose algorithms for discovering community structure in networks, which involve iteratively removing edges based on betweenness measures and recalculating these measures after each removal. They introduce a measure of community strength to determine the optimal number of communities. The algorithms effectively identify community structure in both computer-generated and real-world networks, revealing complex networked systems. The paper discusses various betweenness measures, including shortest-path, random-walk, and current-flow, and their implementation. It also introduces modularity as a metric to quantify the strength of community structure. The algorithms are tested on synthetic networks and real-world data, such as Zachary's karate club network and a collaboration network of scientists. The results show that the algorithms perform well in identifying communities, with the shortest-path betweenness method being recommended for most applications due to its efficiency and effectiveness. The paper highlights the importance of recalculating betweenness measures after each edge removal to ensure accurate community detection.Newman and Girvan propose algorithms for discovering community structure in networks, which involve iteratively removing edges based on betweenness measures and recalculating these measures after each removal. They introduce a measure of community strength to determine the optimal number of communities. The algorithms effectively identify community structure in both computer-generated and real-world networks, revealing complex networked systems. The paper discusses various betweenness measures, including shortest-path, random-walk, and current-flow, and their implementation. It also introduces modularity as a metric to quantify the strength of community structure. The algorithms are tested on synthetic networks and real-world data, such as Zachary's karate club network and a collaboration network of scientists. The results show that the algorithms perform well in identifying communities, with the shortest-path betweenness method being recommended for most applications due to its efficiency and effectiveness. The paper highlights the importance of recalculating betweenness measures after each edge removal to ensure accurate community detection.