Suitability analysis of graph visualization algorithms for personalized study planning

In previous papers the present authors have covered personalized study planning framework, and also have implemented study planning system (SPS) prototype, which allows to create a personalized study program, and then to plan the course learning, setting the courses in the required sequence, and make structure analysis thus detecting the most significant nodes in the graph structure. In this paper authors describe the application of eight common graph visualization algorithms - Tree, Circular, EfficientSugiyama Fruchterman-Reingold - FR, BoundedFR, ISOM, Kamada - Kawai - KK; LinLog - for representing study plans and course structures in the personalized study planning system.