International Journal of Innovative Research in Computer Science & Technology (IJIRCST) ISSN: 2347-5552, Volume-3, Issue-3, May-2015 130 Abstract—A social network is a social structure of people, related (directly or indirectly) to each other through a common relation or interest. Social network analysis (SNA) is the study of social networks to understand their structure and behavior. For studying structural and behavioral properties of these networks, communities are identified by grouping of individuals according to given context into subgroups. Community detection is very rich domain in social network analysis as it is useful in various domains like business, marketing, healthcare etc. Data analytic techniques such as data mining and predictive modeling are being used to gain new insights into social network analysis (SNA). This has the unique ability to play a new role in exploring the context and situations that lead to efficient and effective predictions. Identifying these social communities can bring benefit to understanding and predicting user’s behaviors. This paper is an attempt to study the various approaches for community detection (CD), application area of CD and evaluation of CD algorithm. It also presents the emerging and ongoing research towards improvement in existing CD algorithms in the area of social network analysis. Index Terms— Community Detection, Evaluation of Identified Communities, Healthcare, Overlapping Community Detection, Social Network. I. INTRODUCTION In recent times, user activities on web based social networks has increased enormously irrespective of time and place that generates magnanimous datasets which offers tremendous scope for both mining interesting user behavior and knowledge discovery. Social Networking now a days is considered as one of the most important feature as so many critical; activities are depended on it. In this paper, the basic concept of social networking and various terminologies related to social network are discussed. The study focuses on the concept of social network and community structure which is considered as one of the most important features of social network and also the importance of detecting these communities. In recent years, complex networks such as social networks have received great attention due to their popularity, also the need to understand their structure and their usefulness in several domains such as healthcare, education, marketing and business. Manuscript received May 23, 2015 Seema Rani, Department of Computer Science, Jamia Millia Islamia University, New Delhi, India,+011 9811476855, (e-mail: seema7519@yahoo.com). Monica Mehrotra, Department of Computer Science, Jamia Millia Islamia University, New Delhi, India, 9818846513, (e-mail: drmehrotra2000@gmail.com). The community structure captures the tendency of nodes in the network to group together with other similar nodes into communities. This property has been observed in many real-world networks. Despite excessive studies of the community structure of networks, there is no consensus on a single quantitative definition for the concept of community and different studies have used different definitions. A community, also known as a cluster, is usually thought of as a group of nodes that have many connections to each other and few connections to the rest of the network. Identifying communities in a network can provide valuable information about the structural properties of the network, the interactions among nodes in the communities, and the role of the nodes in each community. Community is groups of vertices are more densely connected than to other vertices in the network. Community detected from the social network provides basic information for other tasks. Community detection methods broadly categorizes into four: Node-Centric Community, Group-Centric Community, Network-Centric Community and Hierarchy-Centric Community [20]. In node centric community each node in a group satisfies certain properties. Group centric community considers the connections within a group as a whole. The group has to satisfy certain properties without going into node-level detail. Network centric community partition the whole network into disjoint sets. Hierarchy-Centric Community constructs a hierarchical structure of communities. II. PRELIMINARIES & RELATED TERMINOLOGIES A. Information graph Let graph G ={V, E} ,where V is the network of individuals (or nodes). V = {v 1 , v 2 ,…v n } contains n nodes. E is a collection of links (or edges) in the network, E ={e 1 ,e 2 ,….e m } . Each node has p attributes. The collection of node V is V att = {a 1 ,a 2 ,….a p }. Node attributes and links matrix of the graph can be constructed by the above information The following matrix can represent the information links: A 11 …………..…… A 1n ……… A ij …………….…… …A ji ………………… A m1 …..…………… A mn wherein A ij is connection information of node i to node j. Identification of Communities from Social Networks Seema Rani, Monica Mehrotra