Social networks are in general dynamically due to the involvement of many people on the web such as Facebook, Twitter, and Snapchat, etc. The meaningful visualization and analysis of social network is challenging due to its dynamic nature, the mobility of nodes in the network and extremely large size. In this paper, we consider the higher dimensionality issue of social networks regarding time series social network construction and visualization. To solve this issue, we develop a statically data-mining based approach for dimensionality reduction in social networks. Basically, we find that each sub-social network’s model has different dimensions by nodes and links which are sampled originally from an m-dimensional metric space. Experimentally, we find that the m-dimensional features for each sub-network cause fail connections in time-series during the network reconstruction model for visualization. Therefore, we propose a new dimension reduction approach that is based on developing an SVD algorithm by relying on select significant subfeatures. Then we extract time features from the feature space of the original dataset to visualize the network in a deferent time interval. However, to monitor the network development and also the dimensionality reduction of features help us to speed up the computation time of the shortest path. The social circle Facebook dataset form Stanford is used with its corresponding attributes. The dataset includes node features (profile), circles, and ego networks. The obtained result shows better performances regarding the computation time and network visualization. Moreover, the experimental results show that the proposed system is much faster than the approach based on the whole feature space for closeness centrality computing.