Correlation analysis of community detection in social network of big data methodical using set theorem

Abstract

A trending issue in the network system that aids in learning and understanding the overall network structure is the community detection in the social network. Actually, they are the dividing wall which divides the node of the network into several subgroups. While dividing, the nodes within the subgroups will get connected densely but, their connections will be sparser between the subgroups. The ultimate objective of the community detection method is to divide the network into dense regions of the graph. But, in general, those regions will correlate with close related entities which can be then said that it is belonging to a community. It is defined based on the principle that the pair of nodes will be connected only if they belong to the same community and if they don’t share the communities, they are less likely to be connected. The vital problems across various research fields like the detection of minute and scattered communities have been necessitated with the ever growing variety of the social networks. The problem of community detection over the time has been recognized with the literature survey and the proposal methodology of set theorem to find the communities detection where the group belongs to activities. In addition to this, several basic concepts are stated in an exhaustive way where the research fields arise from social networks.