Graph Algorithm Visualizer

Abstract

A comprehensive study on algorithm visualization reveals insights into content distribution, creator demographics, and visualization quality. The associated wiki catalogues over 350 algorithm visualizations and includes an annotated bibliology on algorithm visualization literature. Unfortunately, the majority of visualizations are deemed low-quality, with a bias towards simpler topics. The report proposes the development of an e-learning tool focusing on specific visualizations like Pathfinder, Prime Numbers, Sorting Algorithms, N Queen, Convex Hull, and Binary Search Game. The absence of effective repositories for algorithm visualizations is recognized as a significant gap. Emphasizing the need for improved dissemination, the report suggests initiatives to inform developers about existing gaps and requirements within the field. It also underscores the importance of propagating established best practices for creating high-quality visualizations. In conclusion, the report highlights the urgency of addressing these deficiencies to cultivate a more robust and accessible ecosystem for algorithm visualization resources. Despite the challenges, there is a clear call to action to enhance the standard of algorithm visualizations and foster a more inclusive and informed community. Key Words: Algorithm, Visualization, Pathfinder, Sorting, Search Game.