In examining the computer networks, social networks, biological networks and many other natural and artificial networks, community detection plays a vital role. Progressive researches were conducted on problems related to community network analysis, for both academia and industry. It is very difficult to compute the common neighbors shared by between nodes i and j, so we make use of the Enhanced Nearest Neighbor-Based Clustering (ENNC) Algorithm provides an optimal solution. New Enhanced Artificial Bee Colony (EABC) theoretic approaches were conducted towards community detection in large-scale complex networks based on modified modularity; According to the modified adjacency, modified Laplacian matrices and neighborhood similarity, we develop this method. From the experimental result it is confirmed that the improved data structures accelerate the method by a large factor, for large networks, making it competitive with other state of the art algorithm, Output explains that the ENNC - EABC predominantly and constantly beats other methods while verifying our assumptions on the correlations among the vertices and communities.
Volume 11 | 08-Special Issue
Pages: 1492-1504