The paper presents development of the authors’ approach to visualization of graph models of various types based on the use of visualization metaphors and aimed at increasing cognitive clarity of these models. One of the key problems of this approach is investigated – namely, formalization of the process of constructing representation metaphors for graph models. Features of graph models that allow formalizing the process of their visualization are considered, the necessary terminology is introduced. A number of principles have been formulated that must be considered when forming metaphors for representing graph models. On the basis of the introduced principles, a general approach to the construction of representation metaphors for visualization of arbitrary graph models is proposed. The main ideas for applying the proposed approach are demonstrated by examples of constructing representation metaphors for two types of graph models: fuzzy cognitive maps and Bayesian networks. In order to discuss the results, a contradiction between the volume of a representation metaphor and its cognitive clarity is formulated, and a hypothesis is proposed about the relationship of this contradiction with Hick's law. The feasibility of experimental study of this relationship and the refinement of its parameters, including with the aim of developing recommendations for the construction of efficient representation metaphors of graph models, is noted. In the future, the presented approach can become an important component of an integrated approach to building a visualization mechanism for an arbitrary graph model, which provides support for efficient visual analysis throughout all stages of modeling.
