# How to Visualize the Knowledge Graph Generated by Semantica: Complete Developer Guide

> Learn how to visualize the Semantica knowledge graph using KGVisualizer. Explore interactive diagrams, community colorings, and statistical charts in this developer guide.

- Repository: [Semantica /semantica](https://github.com/semantica-agi/semantica)
- Tags: how-to-guide
- Published: 2026-09-04

---

**Semantica's `KGVisualizer` class converts internal `KnowledgeGraph` objects into interactive network diagrams, community colorings, and statistical charts through a standardized 10-step pipeline orchestrated in [`kg_visualizer.py`](https://github.com/semantica-agi/semantica/blob/main/kg_visualizer.py).**

The `semantica-agi/semantica` repository ships with a comprehensive visualization subsystem that transforms abstract knowledge representations into explorable visual formats. Whether you need to inspect entity relationships, analyze community structures, or export publication-ready figures, the toolkit supports multiple output formats including interactive HTML, static PNG, and inline notebook displays.

## KGVisualizer Architecture and Core Pipeline

The visualization workflow centers on the **`KGVisualizer`** class defined in [[`semantica/visualization/kg_visualizer.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/visualization/kg_visualizer.py)](https://github.com/semantica-agi/semantica/blob/main/semantica/visualization/kg_visualizer.py). This orchestrator accepts any dictionary-compatible graph structure containing `"entities"` and `"relationships"` keys, making it compatible with native `KnowledgeGraph` instances, `GraphBuilder` outputs, or custom extraction pipelines.

When you invoke any visualization method, the engine executes a deterministic 10-step pipeline:

1. **Dependency Verification** – The `_check_dependencies` method validates Plotly availability before rendering.

2. **Graph Normalization** – Internal `KnowledgeGraph` objects are converted to plain dictionaries via `_convert_knowledge_graph` and `_normalize_graph`, ensuring consistent schema access.

3. **Node and Edge Extraction** – Helper methods `_extract_nodes` and `_extract_edges` retrieve identifiers, labels, types, and metadata fields required for rendering.

4. **Layout Computation** – Based on the `layout` parameter (`force`, `hierarchical`, or `circular`), the system delegates to algorithms in [[`layout_algorithms.py`](https://github.com/semantica-agi/semantica/blob/main/layout_algorithms.py)](https://github.com/semantica-agi/semantica/blob/main/semantica/visualization/utils/layout_algorithms.py) to generate `(x, y)` coordinates.

5. **Color Mapping** – Node colors resolve from explicit `color` fields or dynamic property mapping (`node_color_by`) using palettes defined in [[`color_schemes.py`](https://github.com/semantica-agi/semantica/blob/main/color_schemes.py)](https://github.com/semantica-agi/semantica/blob/main/semantica/visualization/utils/color_schemes.py) via `ColorPalette.get_entity_type_colors` or `ColorPalette.get_community_colors`.

6. **Size Scaling** – When `node_size_by` is specified, values normalize to a 10–50 pixel range; otherwise static `node_size` applies. Centrality visualizations inject computed scores via `visualize_centrality`.

7. **Hover Data Enrichment** – The engine constructs HTML hover strings combining node labels, types, and additional fields specified in `hover_data`.

8. **Path Highlighting** – Optional `highlight_path` arguments trigger `classify_path_distance` (from [[`semantica/utils/helpers.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/utils/helpers.py)](https://github.com/semantica-agi/semantica/blob/main/semantica/utils/helpers.py)) to style edges by hop distance.

9. **Figure Assembly** – Plotly `Scatter` traces are constructed for background edges, highlighted paths, edge labels, and nodes, with automatic annotation handling for small graphs.

10. **Export or Return** – If `file_path` is provided, [[`export_formats.py`](https://github.com/semantica-agi/semantica/blob/main/export_formats.py)](https://github.com/semantica-agi/semantica/blob/main/semantica/visualization/utils/export_formats.py) handles serialization via `export_plotly_figure` or `export_matplotlib_figure`; otherwise the interactive object returns for immediate display.

## Visualizing Network Topology

The most common use case renders the complete entity-relationship structure as an interactive network diagram.

```python
from semantica.visualization import KGVisualizer

# Initialize with force-directed layout and vibrant color scheme

viz = KGVisualizer(layout="force", color_scheme="vibrant")

# Generate interactive visualization

fig = viz.visualize_network(
    graph,  # KnowledgeGraph instance or compatible dict

    output="interactive",
    node_color_by="type",
    hover_data=["metadata", "confidence"],
)
fig.show()

```

This method leverages the full pipeline, automatically selecting layout algorithms from `ForceDirectedLayout`, `HierarchicalLayout`, or `CircularLayout` based on your configuration.

## Highlighting Communities and Clusters

When analyzing graph topology, community assignments require distinct visual encoding. The `visualize_communities` method accepts pre-computed partition dictionaries and maps them to discrete color palettes.

```python
communities = {
    "node_assignments": {"n1": 0, "n2": 1, "n3": 0},
    "num_communities": 2,
}

viz.visualize_communities(
    graph,
    communities,
    output="html",
    file_path="communities.html",
)

```

The system invokes `ColorPalette.get_community_colors` to assign distinct hues to each detected community, ensuring visual separation between clusters.

## Centrality-Driven Analysis

To emphasize structural importance, pass centrality scores to scale node sizes proportionally.

```python
centrality_data = {
    "centrality": {"n1": 0.7, "n2": 0.2, "n3": 0.5},
}

viz.visualize_centrality(
    graph,
    centrality_data,
    centrality_type="degree",
    output="png",
    file_path="centrality.png",
)

```

The `visualize_centrality` method injects normalized size values into the rendering pipeline, overriding static `node_size` parameters with centrality-weighted scaling within the 10–50 pixel range.

## Statistical Visualizations

Beyond network diagrams, the toolkit generates aggregate statistical charts.

**Entity Type Distribution:**

```python
fig = viz.visualize_entity_types(
    graph,
    output="interactive",
)
fig.show()

```

**Relationship Frequency Heatmaps:**

```python
fig = viz.visualize_relationship_matrix(
    graph,
    output="interactive",
)
fig.show()

```

These methods bypass layout algorithms and instead utilize Plotly's native chart types to reveal dataset composition patterns.

## Configuration and Export Options

The visualization subsystem supports flexible output targets through [`export_formats.py`](https://github.com/semantica-agi/semantica/blob/main/export_formats.py). Specify `output="html"` for standalone interactive files, `output="png"` for static raster images, or `output="interactive"` for Jupyter notebook embedding.

Key configuration parameters include:

- **`layout`**: Selects the positioning algorithm (`force`, `hierarchical`, `circular`)
- **`color_scheme`**: References palettes defined in [`color_schemes.py`](https://github.com/semantica-agi/semantica/blob/main/color_schemes.py)
- **`node_color_by`**: Maps visual encoding to entity properties or community IDs
- **`highlight_path`**: Accepts node sequences for path tracing with distance-based edge styling

## Summary

- The **`KGVisualizer`** class in [`kg_visualizer.py`](https://github.com/semantica-agi/semantica/blob/main/kg_visualizer.py) serves as the central orchestrator for all Semantica knowledge graph visualization tasks.
- A standardized 10-step pipeline handles normalization, layout calculation via [`layout_algorithms.py`](https://github.com/semantica-agi/semantica/blob/main/layout_algorithms.py), color mapping through [`color_schemes.py`](https://github.com/semantica-agi/semantica/blob/main/color_schemes.py), and multi-format export.
- **Five primary visualization modes** support network topology, community detection, centrality analysis, entity distributions, and relationship matrices.
- The system accepts any dictionary-compatible graph with `"entities"` and `"relationships"` keys, ensuring flexibility across `KnowledgeGraph` instances and custom data structures.
- Export options include interactive HTML, static PNG, and inline notebook rendering via `export_plotly_figure` and `export_matplotlib_figure`.

## Frequently Asked Questions

### Can I visualize a custom graph not created by Semantica's KnowledgeGraph builder?

Yes. The `KGVisualizer` operates on plain dictionaries containing `"entities"` and `"relationships"` keys. As long as your custom graph adheres to this schema, the `_convert_knowledge_graph` and `_normalize_graph` methods will process it without requiring native `KnowledgeGraph` instantiation.

### Which layout algorithm performs best for large knowledge graphs with thousands of nodes?

The `force` layout (force-directed) generally produces the most interpretable results for complex, highly-connected graphs, though computation time increases with node count. For hierarchical taxonomies, use `hierarchical` from [`layout_algorithms.py`](https://github.com/semantica-agi/semantica/blob/main/layout_algorithms.py) to leverage directed structure and minimize edge crossings.

### How do I customize the color palette for entity types?

Pass a custom mapping or reference alternative schemes via the `color_scheme` parameter during `KGVisualizer` initialization. The `ColorPalette` class in [`color_schemes.py`](https://github.com/semantica-agi/semantica/blob/main/color_schemes.py) exposes `get_entity_type_colors` for programmatic palette generation, allowing you to override default hues or define domain-specific color mappings.

### Is it possible to export visualizations programmatically without displaying them?

Absolutely. Specify a `file_path` argument in any `visualize_*` method combined with `output="html"` or `output="png"`. The system routes to `export_plotly_figure` or `export_matplotlib_figure` in [`export_formats.py`](https://github.com/semantica-agi/semantica/blob/main/export_formats.py), writing directly to disk and suppressing interactive display.