Community and Outliers Detection in Social Network

Challenges of detecting communities among users' interactions play the popular role for days of Social Network. The previous authors proposed for detecting communities in different point of view. However, similarity based on edge structure and nodes which cannot group into communities are still motivating. Considering the community detection is motivating from the similarity measurement to detect significant communities which are high tightly connected each other upon the edge structure and outliers which are unnecessary to group into the communities. This paper is proposed the approach of using similarity measure based on neighborhood overlapping of nodes to organize communities and to identify outliers which cannot be grouped into any of the communities based on Edge Structure. The result implies the best quality with modularity measurement which leads to more accurate communities as well as improved their density after removing outliers in the network structure.