Node Density and Attraction Detection Method (NDAD) for Community Detection in Complex Network

Community detection plays a vital role in recent research in the analysis of complex network structures. It aims at identifying the node communities with high connectivity. The current research direction finds it a challenging work to accurately detect and split the community in a very large-scale complex network. In this paper, a new systematic way of approach is proposed based on the node density and the internode attraction. The proposed Node Density and Attraction Detection (NDAD) algorithm uses a degree of the nodes and node density to divide the network into communities with similar nodes. Also, the attraction between nodes is calculated using the attraction method. The resultant communities provide valuable insight into the network. The performance evaluation of the node density method and attraction-based method was done by comparing them with the existing algorithms. The network used for the analysis is the Dolphin network. The findings of this analysis provide insights into the strengths and limitations of the node density and attraction-based approach for community detection in complex networks. The empirical result shows that the recommended approach gained improved accuracy and effective community detection.