Social media has followed an exponential graph over the past few years with incorporating features which at one time seemed impossible. The social media has had an enduring effect on the thought process of the general populace. With the diverse nature of the population which take part in the daily chatting, tagging, posting and uploading on the virtual world, the study of such coalesce of communities. This paper aims at the mining and analysis of the communities with focus on the techniques used for the detection process. We discuss four methods of detection, beginning with the node-centric moving on to group centric, then to network centric and concluding with hierarchy centric method of detection. This paper also briefly discusses the applications of community detection in varied fields.Copy Right, IJAR, 2016,. All rights reserved. …………………………………………………………………………………………………….... Introduction:-The past decade has witnessedthe rapid development of social networking sites which has empowered new ways of collaboration and communication. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale [2]. Hence, study of social network is of great importance in sociology, biology and computer science. Social network analysis is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. Social network analysis provides both a visual and a mathematical analysis of human relationships. A valuable tool in the analysis of large complex networks is community detection. Community Detection:-Community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group [1]. There are two types of communities in social networksExplicit groups which are formed as a result of conscious human decision. Implicit groups which emerge from interactions and activities of users. Often communities are defined with respect to a graph, which consists of set of objects called vertices (V) and their relations called as edges (E). Therefore, according to computer science, community detection is identifying a group of vertices that are more densely connected to each other than the rest of the network [1]. Figure below shows a network with three communities.