Force-Directed Graph Layout Based on Community Discovery and Clustering Optimization

In order to visualize the important information in the knowledge graph and visualize the graph data constituting the knowledge graph for visual analysis, this paper optimizes and combines the Louvain algorithm and the force-directed graph algorithm to propose a force-directed graph layout based on community discovery and clustering optimization for the graph data. This paper uses the pruning idea to optimize the calculation steps and the community merging in the Louvain algorithm and obtains a community discovery algorithm that is more efficient and more conducive to optimizing the effect of graph layout, and introduces group elements into the force-directed graph layout to represent the community structure in graph data and implement group-based clustering optimization, so that the force-directed graph layout can clearly display the discovered community structure analyzed by the community discovery algorithm when displaying graph data, and optimize the effect and readability of the graph layout for visual analysis.