A Survey on Community Detection Algorithm and Its Applications
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Abstract
Modern network science has made great improvements in the analysis of a large dynamic world. The existence of a community structure is one of the most prominent factors in these networks. Many algorithms have been proposed to detect structural characteristics and dynamic behaviour of networks over recent years. In this paper, present such a detailed study of recent community detection algorithm techniques such as clustering, modularity, dynamic, overlapped, etc based on various factors and their task in the analysis of the social network. Community detection enables us to evaluate participants with mutual interests or to discover a set of similar people on the basis of an area of interest, proposed a node influence k-nearest neighbours (NI-KNN) algorithm for detecting the community. Community detection is useful in many applications such as Recommendation Systems, Health care, politics, economics, e-commerce, social media, communication network, etc. A comparative analysis of different methods of community detection is also reported.
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