Mental Map-Preserving Visualization through a Genetic Algorithm

The video game industry has evolved significantly, with different genres becoming popular over time, but how to visualize such information by curating data into a form that makes it easier to identify and understand the trends is quite an interesting research topic. This research focuses on producing an animation of aesthetically pleasing two-dimensional (2D) undirected graphs based on PC video game datasets. The data are further analyzed for developing a web-based application giving users the ability to control and create the animation of a graph. To make it easier to understand the animation of a graph, the changes between the displays of the previous and the following periods are set as small as possible, allowing a user to grasp the differences of the graph's structure faster. A genetic algorithm-based undirected graph drawing that minimizes both the aesthetic criteria and mental map cost are proposed in this research to tackle this problem. Furthermore, based on our experiments, we could find the best period to start with, so we do not necessarily need to start from the first period to calculate the animation result. Our experiment results proved that a smoother animation could be achieved, and information is better preserved throughout the animation.