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GraphRAG generates GraphML files that can be visualized using graph visualization tools like Gephi. This guide walks you through the complete process of creating beautiful, insightful visualizations of your knowledge graph.

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Overview

Visualizing your knowledge graph helps you:
  • Understand structure: See how entities and relationships are organized
  • Identify clusters: Discover communities and themes in your data
  • Debug issues: Spot problems with entity extraction or relationships
  • Communicate insights: Share visual representations with stakeholders

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Prerequisites

Before visualizing your graph, you need:
  • A completed GraphRAG index with GraphML snapshots enabled
  • Gephi installed on your system
  • The Leiden Algorithm plugin for Gephi

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Enable GraphML snapshots

GraphML snapshots must be enabled during indexing to generate visualization files.
1

Configure settings.yaml

Open your settings.yaml and ensure GraphML snapshots are enabled:
settings.yaml
snapshots:
  graphml: true
This setting is enabled by default in newly initialized projects.
2

Run the index

graphrag index --root ./my-project
This generates a graph.graphml file in your output directory.
3

Locate the GraphML file

After indexing completes, find the file at:
./my-project/output/graph.graphml
If you’ve already run indexing without GraphML enabled, you’ll need to re-run the index with the setting enabled.

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Visualization workflow

Follow these steps to create a professional graph visualization:

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1. Import into Gephi

1

Launch Gephi

Open Gephi on your system.
2

Import the GraphML file

  1. Go to File → Open
  2. Navigate to your output folder
  3. Select graph.graphml
  4. Click Open
You’ll see a basic visualization of your graph nodes and edges.

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2. Install Leiden Algorithm plugin

The Leiden algorithm detects communities in your graph, which is essential for meaningful visualization.
1

Open Plugin Manager

Go to Tools → Plugins
2

Find Leiden Algorithm

  1. Click the Available Plugins tab
  2. Search for “Leiden Algorithm”
  3. Check the box next to it
  4. Click Install
3

Restart Gephi

Restart Gephi after installation completes

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3. Run statistics

Generate statistics that will help you visualize the graph structure.
1

Calculate Average Degree

In the Statistics panel (right side):
  1. Find Average Degree
  2. Click Run
  3. Click Close on the report dialog
2

Run Leiden Algorithm

In the Statistics panel:
  1. Find Leiden Algorithm
  2. Click Run
  3. Configure settings:
    • Quality function: Modularity
    • Resolution: 1.0
  4. Click OK
  5. Close the report when complete
The Leiden algorithm identifies communities (clusters) in your graph. Higher resolution values create more, smaller communities.

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4. Color nodes by cluster

Color-code nodes based on their community membership.
1

Open Appearance panel

Find the Appearance panel in the upper left
2

Configure node colors

  1. Click the Nodes tab
  2. Click Partition (not Ranking)
  3. Click the color palette icon in the upper right
  4. Select Cluster from the dropdown
3

Generate color palette

  1. Click Palette…
  2. Click Generate…
  3. Uncheck Limit number of colors
  4. Click Generate
  5. Click OK
4

Apply colors

Click Apply to color your graph
Your graph should now show different colored clusters representing communities in your knowledge graph.

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5. Resize nodes by degree centrality

Make important nodes (with many connections) larger.
1

Select ranking mode

In the Appearance panel:
  1. Ensure Nodes is selected
  2. Click Ranking (not Partition)
  3. Click the sizing icon (three circles of different sizes)
2

Configure node sizes

  1. Select Degree from the dropdown
  2. Set Min size: 10
  3. Set Max size: 150
3

Apply sizing

Click Apply
Nodes with more connections will now appear larger, making hubs visually prominent.

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6. Layout the graph

Arrange nodes spatially to reveal structure.

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Step 1: OpenORD layout

1

Select OpenORD

In the Layout panel (lower left), select OpenORD
2

Configure settings

Set the following stage iterations:
  • Liquid: 50
  • Expansion: 50
  • Cooldown: 0
  • Crunch: 0
  • Simmer: 0
3

Run layout

  1. Click Run
  2. Watch the progress bar
  3. Click Stop when complete
OpenORD does initial positioning. The graph may still look messy - that’s expected.

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Step 2: ForceAtlas2 layout

1

Select ForceAtlas2

In the Layout panel, select Force Atlas 2
2

Configure settings

Adjust the following settings:
  • Scaling: 15
  • Dissuade Hubs: ✓ checked
  • LinLog mode: ✗ unchecked
  • Prevent Overlap: ✓ checked
3

Run layout

  1. Click Run
  2. Watch as nodes settle into position
  3. Click Stop when nodes stop moving significantly
This may take several minutes for large graphs. Be patient!
Your graph should now have a clean, organized layout with distinct communities.

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7. Add labels (optional)

Display entity names on the visualization.
1

Show labels

In the bottom toolbar of the graph view, click the Show node labels button (“T” icon)
2

Configure label appearance

  1. Click the label settings button
  2. Adjust:
    • Font size: Based on your preference
    • Show labels: For visible nodes only
    • Label color: Black or contrasting color
3

Resize labels (optional)

You can also size labels by node degree for better readability
For large graphs, consider showing labels only for important nodes (high degree) to avoid clutter.

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Understanding your visualization

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Graph elements

Nodes

Represent entities extracted from your documents (people, organizations, concepts)

Edges

Represent relationships between entities

Colors

Indicate communities - groups of closely related entities

Size

Indicates centrality - how many connections an entity has

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Interpreting patterns

Dense clusters: Topics or themes with many interconnected entities Bridge nodes: Entities connecting different communities (often important cross-cutting concepts) Peripheral nodes: Mentioned infrequently or in isolation Star patterns: Central entities with many direct connections (key people, organizations, or concepts)

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Export and share

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Export as image

1

Open Preview

Click the Preview tab at the top of Gephi
2

Configure preview

Adjust settings for best appearance:
  • Preset: Default
  • Background color: White
  • Show labels: As desired
3

Export

  1. Click Export: SVG/PDF/PNG
  2. Choose format (PNG for presentations, SVG for editing)
  3. Set resolution (high for publications)
  4. Save file

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Export interactive version

Gephi can export interactive web visualizations:
  1. Install the Sigma Exporter plugin
  2. Go to File → Export → Sigma.js template
  3. Configure and export to create an interactive HTML visualization

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Advanced techniques

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Filter by community

Focus on specific communities:
1

Open Filters panel

Find the Filters panel on the right
2

Add partition filter

  1. Expand Attributes → Partition
  2. Drag Cluster to the Queries area
3

Select communities

Check/uncheck clusters to show/hide communities
4

Apply filter

Click Filter to update the visualization

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Size by other metrics

You can size nodes by different centrality measures:
  • Betweenness centrality: Nodes that connect different parts of the graph
  • Closeness centrality: Nodes close to all others
  • Eigenvector centrality: Nodes connected to other important nodes
Run these under Statistics → Network Overview, then use them in the Appearance panel.

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Multi-level analysis

GraphRAG’s Leiden algorithm creates hierarchical communities. To visualize different levels:
  1. Run Leiden multiple times with different resolution parameters
  2. Create separate visualizations for each level
  3. Compare to see how communities nest within each other

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Troubleshooting

Solution:
  • Run ForceAtlas2 longer (it may take time to untangle)
  • Increase Scaling parameter to 20-30
  • Enable Prevent Overlap
  • Try Fruchterman Reingold layout instead
Solution:
  • Ensure you ran the Leiden Algorithm
  • Check that you selected Partition not Ranking
  • Verify Cluster appears in the dropdown
  • Re-run Leiden if needed
Solution:
  • Verify snapshots.graphml: true in settings.yaml
  • Re-run indexing with snapshots enabled
  • Check the storage.base_dir setting for output location
Solution:
  • Increase Gephi’s memory allocation
  • Filter the graph to show fewer nodes
  • Use a more powerful machine for large graphs
  • Consider sampling your data before indexing

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Example workflow summary

Here’s the complete process at a glance:
1

Enable GraphML

snapshots:
  graphml: true
2

Run indexing

graphrag index --root ./my-project
3

Import to Gephi

Open output/graph.graphml
4

Install Leiden plugin

Tools → Plugins → Leiden Algorithm
5

Run statistics

  • Average Degree
  • Leiden Algorithm (Modularity, Resolution 1.0)
6

Apply appearance

  • Color by Cluster (Partition)
  • Size by Degree (10-150)
7

Layout

  • OpenORD (Liquid 50, Expansion 50)
  • ForceAtlas2 (Scaling 15, Dissuade Hubs, Prevent Overlap)
8

Export

Preview → Export as PNG/SVG

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Next steps

Configuration

Learn about all configuration options

Best practices

Optimize your GraphRAG workflow

Query methods

Learn different search approaches

Data model

Understand the output schema

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