A Modularity Maximization Algorithm for Community Detection in Social Networks with Low Time Complexity

Graph 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, whereas large-scale networks have become ubiquitous in real world. We proposed a method that can find communities of a graph with good time and space complexity and good accuracy as well.