A Survey on Community Detection Methods and its Application in Biological Network

Social networks and their analysis is found to be very important now a days. A graph is considered to be the suitable way to represent such social networks. These networks can be represented as a graph in which node represents an individual and edges connecting nodes represent relationship between the individuals. Detection of communities in social networks is advantageous for different applications. This knowledge is valuable to develop solutions to the problems in the area of social network. Community shows that there is dense connection among the node within the group and between group connection is sparse. In the recent years, several algorithms for community detection have been developed. This paper outlines community detection techniques, which have already been proposed. It talks about the prominent community detection algorithms that detect both disjoint and overlapped communities and also some of the traditional algorithms for detecting communities in static and dynamic (real-world) networks. Dynamic network shows remarkable changes as compared to static one. Community detection in dynamic network is very challenging task as the network evolve continuously. In this paper different methods of community detection are analyzed considering type of community structure identified and type of network it is identified from. Community Detection Method also has applications in biological network to determine disease progression.