Community detection in social networks using hybrid merging of sub-communities

Network vertices are often divided into groups or communities with dense connections within communities and sparse connections between communities. Community detection has recently attracted considerable attention in the field of data mining and social network analysis. Existing community detection methods require too much space and are very time consuming for moderate-to-large networks. We propose a bottom up community detection method in which starting with fine-grained communities we find real communities of a network. Merging preliminary small communities is done in a hybrid way to maximize two quality functions: modularity and NMI. We show that our way of community detection is better or as effective as the other community detection algorithms while it has better time and space complexity.