Discovering the Community Structures in the Evolving Multidimensional Social Networks

Online Social network is growing to large extent to share information between the different diversity people around the world. The main objective of the proposed system to identify the community in the multidimensional data such as users , Tags , stories , locations ,employment details ,photos and comments . We propose a data mining technique to detect the frequently interacting users based on the common subjects and grouping them in single community. The main incorporation of the work is to identify a seed-based community in a multi-dimensional network by evaluating the affinity between two items in the same type of entity (same dimension) or different types of entities (different dimensions) from the network. Our idea is to calculate the probabilities of visiting each item in each dimension, and compare their values to generate communities from a set of seed items and explore the feature evolution from the seed item in the same dimension or different dimension. We also propose the friend suggestion system and information suggestions system based on the data relevancy for the items in the different dimensions using seed of the data selection. In order to evaluate a high quality of generated communities by the proposed algorithm, we develop and study a local modularity measure of a community in a multi-dimensional network. Experimental results prove that proposed system outperforms the state of approach in terms of precision and recall.