Community structure detection in complex network based on MST and modularity

This research introduces a new splitting and merging method to identify community structure in networks. This proposed approach relies on constructing a minimal spanning tree (MST) based on the dissimilarities between nodes in a graph and optimizing the modularity function. The Minimum Spanning Tree (MST) of a graph is detached by deleting edges with high dissimilarity values between nodes during the splitting phase. The method identifies the initial community structure after completing this stage. The subsequent stage involves a merging procedure. Communities are progressively combined to identify the optimal community structure with a high modularity value. This proposed approach is parameterless. This study offers a comprehensive structure for executing this strategy. The suggested method was tested on computer-generated networks and several real-world networks, demonstrating its usefulness through experimental results.