Communities are the most meaningful structures in dynamic networks. Tracking this evolution provides insights into the patterns of community evolution in networks over time and valuable information for decision support systems in many research areas such as marketing, recommender systems, and criminology. Previous work has focused on either high accuracy or time efficiency, but not on low memory consumption. This motivates us to develop a method that combines highly accurate tracking results with low computational resources. This dissertation first provides a brief overview of research in dynamic network analysis. Then, a novel space-efficient method, called TREC, for tracking the evolution of communities in dynamic networks is presented, where community matching using LSH with minhasing technique is proposed to efficiently track similar communities in terms of memory consumption over time. The accuracy of TREC is evaluated on benchmark datasets, and the execution time performance is measured on real dynamic datasets. In addition, a comparative algorithmic complexity analysis of TREC in terms of space and time is performed. Both theoretical and experimental results show that TREC outperforms competitor methods on both datasets in terms of combination of space, accuracy, and execution time. Next, it is investigated that whether the TREC method is suitable for predicting the evolution of community areas. In this evaluation, a prediction study is conducted. A common methodology is followed which includes main steps such as feature extraction, feature selection, classifier training and cross validation. Experimental results show that TREC method is suitable for predicting evolution of communities.