GN Algorithm in Complex Networks

In complex network theory, the Girvan-Newman algorithm (GN algorithm) is a community detection method used to identify community structures within networks. This algorithm operates based on the concept of edge betweenness, which measures the importance of an edge in connecting different communities across the network. The GN algorithm iteratively removes edges with the highest betweenness scores while recalculating node connectivity, continuing this process until the network splits into the desired number of communities. From an implementation perspective, the algorithm typically involves: 1. Calculating betweenness centrality for all edges 2. Removing the edge(s) with maximum betweenness 3. Recomputing connected components after each removal 4. Repeating steps 1-3 until termination conditions are met The algorithm serves as one of the most widely-used methods in community detection, enabling the identification of subnetworks and subgroups. Key applications include social network analysis, biological system modeling, and financial network studies, where understanding modular structures provides crucial insights into system organization and dynamics.