An application of optimization method for storyline based on cluster analysis

As a new visualization technology¹, storyline intuitively illustrates the dynamic relationships between entities in a story, which is useful in many applications, including the description of characters' interactions in movies, the evolution of community structure in dynamic social networks, the marital status between people, etc. Previous works optimize the storyline's layout from the perspective of aesthetic standard, significantly reducing line crossings, line wiggles and layout space. But when dealing with large-scale data, there is room for improvement with regard to three issues: insufficient memory space, large time consumption and weak data expression. Therefore, this paper introduces the idea of cluster analysis to the storyline to present the clustering information and reduce the time complexity under the condition where a large number of entities interact in the same period. Meantime a scalable, reusable visualization library of storyline is implemented including some novel interactions.