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GENERATING A HIGH-DIMENSIONAL NETWORK GRAPH FOR DATA VISUALIZATION UTILIZING LANDMARK DATA POINTS AND MODULARITY-BASED MANIFOLD TEARING
GENERATING A HIGH-DIMENSIONAL NETWORK GRAPH FOR DATA VISUALIZATION UTILIZING LANDMARK DATA POINTS AND MODULARITY-BASED MANIFOLD TEARING
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机译:利用地标数据点和基于模块化的歧管撕裂生成数据可视化的高维网图
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摘要
The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate interactive visual shape representation of digital datasets. For example, the disclosed systems can generate an augmented nearest neighbor network graph from a sampled subset of digital data points using a nearest neighbor model and witness complex model. The disclosed system can further generate a landmark network graph based on the augmented nearest neighbor network graph utilizing a plurality of random walks. The disclosed systems can also generate a loop-augmented spanning network graph based on a partition of the landmark network graph by adding community edges between communities of landmark groups based on modularity and to complete community loops. Based on the loop-augmented spanning network graph, the disclosed systems can generate an interactive visual shape representation for display on a client device.
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