A visualization optimization solution for data analysis:
- Dynamic screening technology: Use the Graph Filter panel in the Web interface, which can be accessed via:
- Node degree centrality (screening top 10% key nodes)
- Edge weighting thresholds (e.g., only show connections with correlation > 0.7)
- Community discovery algorithms (auto-coloring clustered groups) - Layout algorithm selection::
- ForceAtlas2 algorithm is recommended for large sparse graphs (config.yaml set layout: "force")
- Hierarchical data applying hierarchical layout (layout: "hierarchy") - Interactive Exploration::
- Hold down the Shift key to frame groups of nodes
- Right-click on a node to see the "secondary associations".
- Use the mouse wheel to zoom in and out at different levels of granularity - Exporting and labeling::
Support PNG/SVG/GEXF three export formats, you can add customized markup text before exporting.
Case in point: When analyzing e-commerce user behavior data, it is recommended to first build an isomorphic map according to the three-layer structure of "user-commodity-category", and then identify potential user groups through the community discovery function.
This answer comes from the articleNodeRAG: A Heterogeneous Graph-Based Tool for Accurate Information Retrieval and GenerationThe
May not be reproduced without permission:AI productivity tools " How can NodeRAG be used to improve the visualization of complex relationships in data analysis scenarios?
