An Improved Force-Directed Algorithm Based on Emergence for Visualizing Complex Network

Visualization of complex network is one of the most important and difficult issues in complexity science. Most current visualization algorithms are based on drawing aesthetically and it’s difficult for them to find the structure information of complex network. To solve this problem, the Fruchterman-Reingold (FR) algorithm based on the force-directed layout is studied in this paper, which is most suitable for the visualization of complex network. From the emergent characteristic of complex network, an improved adaptive FR algorithm is proposed to reduce dependence on parameters in the FR algorithm. In the improved algorithm, the impact of the clustering coefficient on the attraction–repulsion between vertices is considered, and the clustering coefficient is thought to be a determinative indicator for emergence. Then, with the attraction–repulsion the topological characteristic of complex network is visualized. Experiments show that the improved algorithm makes the observation of the structure of complex network much easier. In addition, the improved algorithm displays superior stability and adaptability during experiments.