Graphical framework for scientific papers clustering

Data visualization traditionally is the most powerful tool for demonstration and analysis of scientific results and mathematical models in particular. In this paper we introduce the graphical framework for citation graph clustering. Furthermore, we discuss ways to detect factors responsible for scientific groups formation. Two datasets of scientific papers related to different fields were used in this work. Firstly we applied scientometric analysis to our data with the view to determine the most influential keywords. After that, we used two different ways for data clustering - graphic clustering method comprising N-body communication graph and a keyword-based hierarchical clustering. As a result of our studies we propose method for dynamic visualization of scientific papers clusters, built using open-access data.