With the immense use of mobile devices and online services in the world, the online social interaction in social networks is getting high attention. A billion of users are attracted towards social networking sites and generates a massive social networking data. That provides both opportunities and challenges for analyzing the user's behavior. The users of social networks are structured in the form of community. Community means a group of nodes (users of social network) who posses dense

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... ster relations than intra-cluster relations in the network. Community detection and analysis is needed to understand the social network structure. Community identification unveils properties shared by nodes like common work area, common interest, sports etc. Also, the community detection could be useful in several applications like viral marketing, recommendation system, group search and tracking. This paper deals with the introduction of variety of social networks types, community structure and its type along with comparison between few prominent community detection algorithms. Paper provides insight into applications of community detection in social network analysis, with further research directions.