A method for quantitatively evaluating the degree of visual clutter of MSV

Abstract

The invention discloses a calculation method for quantitatively evaluating the edge level visual confounding degree index of an MSV, including such steps as 1) obtaining any edge e in MSV, calculatingindistinguishable pixel distance value IPD, respectively extending width of IPD from center position of edge e to left and right direction to form indistinguishable pixel area IPA, and calculating indistinguishable pixel distance value IPD from center position of edge e to left and right direction to form indistinguishable pixel area IPA; 2) adding an edge interleaved with that edge e in the IPAinto an interleaved edge set; 3) decomposing the set of interleaved edge according to the node order to obtain a plurality of equidistant and adjacent node pair sets, eliminating overlap cover by using union operation to obtain the set of interleaved edges eliminating overlap cover; 4) calculating the visual mixing degree index of the edge e according to the set of interlaced edges and node pairsto eliminate coverage. The invention quantitatively evaluates the visual mixing degree of the edges in the MSV, which is helpful to provide reliable basis for the subsequent edge sampling work, further helps to reduce the visual mixing degree in the MSV, and improves the readability of the MSV.

Description

technical field [0001] The invention relates to the field of visual optimization of large-scale sequence view (MSV) in dynamic network visualization, in particular to a calculation method for quantitatively evaluating the edge-level visual confusion index of MSV. Background technique [0002] Network (Network) is also called graph (Graph). Generally, all entities with relationships can be abstracted into a network (graph), that is, entities are abstracted into nodes, and the relationship between entities is abstracted into edges. According to whether the network will change over time, it can be divided into static network and dynamic network. [0003] The dynamic network is mainly used to represent the change of nodes in the network and the relationship between nodes with time. In order to efficiently and intuitively help people understand the behavior information of time-varying networks, a common practice is to implement visual mapping and layout design that meet aestheti...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem, most of the existing edge sampling technologies consider static graph sampling, and few algorithms consider dynamic graph sampling.

Moreover, none of the existing MSV improvement technologies is done by sampling, and the improved results will introduce new defects (such as the introduction of edge crossing, etc.)

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