Drawing large weighted graphs using clustered force-directed algorithm

![CDATA[Clustered graph drawing is widely considered as a good method to overcome the scalability problem when visualizing large (or huge) graphs. Force-directed algorithm is a popular approach for laying graphs yet small to medium size datasets due to its slow convergence time. This paper proposes a new method which combines clustering and a force-directed algorithm, to reduce the computational complexity and time. It works by dividing a Long Convergence: LC into two Short Convergences: SC1, SC2, where SC1+SC2 < LC. We also apply our work on weighted graphs. Our experiments show that the new method improves the aesthetics in graph visualization by providing clearer views for connectivity and edge weights.]]