A Comparative Study of Community Detection Algorithms using Graphs and R

In today's world, the communication and interaction among the people is through the social networks. Graphically these networks can be represented as the collection of nodes and edges. People belong to different communities depending upon their relations. The community detection in networks is the clustering of networks into communities in such a way that the connections between nodes within the same community are strong as compared to the connections between the nodes across the community. In this paper, different approaches in the field of community detection have been described and are compared on the basis of modularity using datasets of real world networks of varying sizes.