Semantic-enhanced large-scale multi-element graph simplified visualization method

Abstract

The invention discloses a semantic-enhanced large-scale multi-element graph simplification visualization method, which comprises the following steps of: establishing a large-scale multi-element graph,and extracting a hierarchical structure of the large-scale multi-element graph; constructing a multi-scale community set according to the hierarchical structure of the large-scale multi-element graphby utilizing the attributes of the large-scale multi-element graph, the attributes of the large-scale multi-element graph including modularity and multi-dimensional attribute information entropy; constructing a multi-level force guiding layout for the multi-scale community set according to the hierarchical structure of the large-scale multivariate graph, and displaying the semantic expression ofthe communities through mapping; And using the community after mapping display to obtain a hierarchical view and an attribute mulberry-based view, and performing visual analysis on the large-scale multi-element view by using a multi-level force guide layout, the hierarchical view and the attribute mulberry-based view. According to the method, the visual expression of the large-scale multi-elementgraph can be effectively simplified, the association structure and semantic composition of the large-scale multi-element graph in different application fields can be rapidly analyzed, and the practicability is high.

Description

technical field [0001] The invention relates to a simplified visual analysis method for a large-scale multivariate graph, belonging to the graph visualization field. Background technique [0002] Network graph visualization can effectively display the connection relationship between network nodes, and is widely used in many fields, such as social networks, knowledge graphs, biological gene networks, etc. As the scale of network data continues to increase, how to simplify and express large-scale network graph structures has become a research hotspot in the field of graph visualization. Classic network graph simplification visualization methods mainly include technologies such as graph sampling, edge binding, and graph clustering. On the basis of reducing the visual disorder caused by a large number of intersections of points and lines, it improves the efficiency of users' exploration and cognition of large-scale network structures. However, the above methods mainly focus on ...

AI Technical Summary

Problems solved by technology

However, traditional simplified visualization methods for large-scale network graphs are difficult to make full use of the multi-dimensional attribute information of network nodes and extract effective semantic knowledge, thus failing to help users better understand the semantic relationship between topology and multi-dimensional attributes, and cannot deeply explore network characteristics. Wait

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