Analysis of Community Detection Algorithms

Community Detection is starting to become an inevitable task for data science community and like-minded analysts to understand the hidden insights and trends in large complex networks. Through this paper we would like to understand and comparatively analyze the prevalent and recent community detection techniques like Minimum Cut Method, Hierarchical Clustering, Girvan Newman Algorithm, Modularity Maximization, Statistical Inference, Clique Percolation & Maximum Permanence Method based on their network relevance, adaptability and robustness to changes and finally their capacity of size and speed. Finally, we will review the realworld application of these algorithms.