Comparative analysis of community detection algorithms

Rapid growth in data has caused a sudden surge of interest among researchers to study the network structure for community detection. In this paper, we have provided a comparative analysis of community detection algorithms for complex networks. In comparison with earlier work on community detection, our work presents the analysis on real network data instead of using synthetic data. This analysis has been performed in two phases. The first phase uses small and medium networks (nodes:10k, edges:20k) and is a selection phase to extract best performing algorithms. The second phase identifies the best community detection algorithm through evaluation on larger complex networks(nodes:100k, edges:1000k).