Illustrating data visually through graphs enables the examination of valuable insights and the identification of crucial patterns that are beneficial for the user. However, as the volume of data grows, it becomes increasingly challenging to organize corresponding graphs and zero in on specific objects and/or relations of interest. Various techniques have been developed to tackle the complexity of large graphs, yet these methods operate independently, potentially resulting in inconsistencies and incorrect behaviors, as well as inefficient use of computing resources when mixed together and applied in a certain order. Furthermore, administering these methods can lead to significant changes in the layout of the graph, potentially disorienting the user and disrupting their mental map. This study aims to design a framework and associated algorithms for efficiently and effectively managing complexity during visual analysis of large relational data represented as graphs, by seamlessly integrating various complexity management techniques and making adjustments to the graph layout after each operation to preserve the user’s mental map. A rendering-independent implementation of this framework and associated algorithms, as well as its integration with a popular graph rendering library named Cytoscape.js, can be accessed freely on GitHub .