semantica/docs/reference/visualization.md

title, description, icon

title	description	icon
Visualization Module	Interactive and static knowledge graph, ontology, embedding, and temporal visualization.	chart-bar

semantica.visualization renders knowledge graphs, ontologies, embedding spaces, and temporal data as interactive HTML or static images: without launching the full Explorer server:

• KGVisualizer: interactive network with force, hierarchical, and circular layouts
• EmbeddingVisualizer: 2D/3D UMAP or t-SNE projections with cluster labels
• TemporalVisualizer: timeline views and graph evolution across snapshots
• AnalyticsVisualizer: centrality scores, community structure, degree distribution charts

Requires plotly: pip install plotly. Some exporters also need matplotlib or graphviz.

Exported Classes

Class	Role
KGVisualizer	Interactive network, community, and subgraph rendering with force/hierarchical/circular layouts
OntologyVisualizer	Class hierarchy and property relationship diagrams from any ontology
EmbeddingVisualizer	2D/3D UMAP or t-SNE projection of embedding spaces with cluster labels
SemanticNetworkVisualizer	Weighted semantic network rendering
AnalyticsVisualizer	Centrality scores, community structure, connectivity, and degree distribution charts
TemporalVisualizer	Timeline views and graph evolution across snapshots

Quick Start

```python from semantica.visualization import KGVisualizer

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

# Interactive: opens in browser, supports hover and click
viz.visualize_network(graph, output="interactive")
```

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

viz.visualize_network(
    graph,
    output="html",
    file_path="graph.html",
    node_color_by="type",      # color nodes by entity type attribute
)
```

```python # Static PNG: for reports and embedding in documents viz.visualize_network(graph, output="png", file_path="graph.png")

# Vector SVG: for publications and scalable diagrams
viz.visualize_network(graph, output="svg", file_path="graph.svg")
```

Visualizers

Interactive and static knowledge graph rendering:

```python
from semantica.visualization import KGVisualizer

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

# Interactive: opens in browser
viz.visualize_network(graph, output="interactive")

# Save as HTML file
viz.visualize_network(graph, output="html", file_path="graph.html")

# Static PNG
viz.visualize_network(graph, output="png", file_path="graph.png")

# Community-colored graph
viz.visualize_communities(graph, communities, file_path="communities.html")

# Centrality-sized nodes
viz.visualize_centrality(graph, centrality, centrality_type="degree")

# Entity type distribution bar chart
viz.visualize_entity_types(graph, output="interactive")

# Relationship frequency heatmap
viz.visualize_relationship_matrix(graph, output="interactive")
```

**Layout options (`layout=`):**

| Layout | Description | Best For |
| :------ | :----------- | :-------- |
| `force` | Physics simulation: clusters emerge naturally | General graphs |
| `hierarchical` | Top-down tree layout | Taxonomies, org charts |
| `circular` | Nodes on a circle, edges as chords | Small dense graphs |

Visualize class hierarchies and property relationships:

```python
from semantica.visualization import OntologyVisualizer

viz = OntologyVisualizer()

# Class hierarchy tree
viz.visualize_hierarchy(ontology, output="interactive")

# Property domain/range graph
viz.visualize_properties(ontology, output="html", file_path="properties.html")

# Full structure network (classes + properties)
viz.visualize_structure(ontology, output="interactive")

# Class-property matrix heatmap
viz.visualize_class_property_matrix(ontology, output="html", file_path="matrix.html")

# Ontology metrics dashboard
viz.visualize_metrics(ontology, output="interactive")
```

Project high-dimensional embeddings into 2D for cluster analysis:

```python
from semantica.visualization import EmbeddingVisualizer

viz = EmbeddingVisualizer()

viz.visualize_2d_projection(
    embeddings=embeddings,
    labels=labels,
    output="interactive",
    file_path="embeddings.html",
    method="umap",    # "umap" | "tsne" | "pca"
)
```

| Method | Speed | Preserves | Best For |
| :------ | :----- | :--------- | :-------- |
| `umap` | Fast | Global + local structure | Large datasets, cluster discovery |
| `tsne` | Medium | Local structure | Tight cluster separation |
| `pca` | Very fast | Variance | Quick overview, linear structure |

Visualize how a knowledge graph changes over time:

```python
from semantica.visualization import TemporalVisualizer

viz = TemporalVisualizer()

# Timeline of entity/relationship changes
viz.visualize_timeline(temporal_data, output="interactive")

# Animated network evolution: one frame per time step
viz.visualize_network_evolution(temporal_kg, output="html", file_path="evolution.html")

# Side-by-side snapshot comparison
# snapshots: dict mapping timestamp strings to graph dicts
snapshots = {
    "2023-01": graph_v1,
    "2024-01": graph_v2,
}
viz.visualize_snapshot_comparison(snapshots, output="html", file_path="diff.html")

# Temporal patterns: pass a list of pattern dicts
viz.visualize_temporal_patterns(patterns, output="html", file_path="patterns.html")

# Metrics evolution over time
viz.visualize_metrics_evolution(metrics_history, timestamps, output="interactive")
```

Visualize graph analytics results: centrality, communities, and degree distribution:

```python
from semantica.visualization import AnalyticsVisualizer

viz = AnalyticsVisualizer()

# Bar chart of top-N nodes by centrality measure
# param is centrality_type= (not metric=) and top_n= (not top_k=)
viz.visualize_centrality_rankings(
    centrality,
    centrality_type="pagerank",
    top_n=20,
    output="html",
    file_path="centrality.html",
)

# Community-colored network graph
viz.visualize_community_structure(kg, communities, output="html", file_path="communities.html")

# Degree distribution histogram
viz.visualize_degree_distribution(kg, output="html", file_path="degree_dist.html")

# Connectivity analysis (connected/disconnected, component sizes)
viz.visualize_connectivity(connectivity, output="interactive")

# Full metrics dashboard (nodes, edges, density, diameter)
viz.visualize_metrics_dashboard(metrics, output="interactive")

# Compare multiple centrality measures side-by-side
viz.visualize_centrality_comparison(centrality_results, top_n=10)
```

Color Schemes

All visualizers accept a color_scheme= constructor parameter:

viz = KGVisualizer(color_scheme="vibrant")

Scheme	Description	Best For
default	Blue-grey palette	General use
vibrant	High-contrast, saturated colours	Presentations
pastel	Soft, muted tones	Light backgrounds
dark	Dark background with bright nodes	Dark-mode dashboards
light	White background, thin edges	Publications, print
colorblind	Okabe-Ito safe palette	Accessibility

Export Formats

Format	Interactive	Scalable	Best For
.html	Yes	N/A	Web dashboards, exploratory analysis
.png	No	No	Reports, Jupyter notebooks
.svg	No	Yes	Publications, slide decks
.pdf	No	Yes	Print, compliance exports

Convenience Functions

from semantica.visualization import (
    visualize_kg, visualize_ontology, visualize_embeddings,
    visualize_semantic_network, visualize_analytics, visualize_temporal,
)

# Returns Plotly figure or None
fig = visualize_kg(graph, output="interactive", method="default")
fig = visualize_ontology(ontology, output="interactive", method="hierarchy")
fig = visualize_embeddings(embeddings, labels, output="interactive", method="2d_projection")
fig = visualize_analytics(analytics_data, output="interactive", method="centrality")
fig = visualize_temporal(temporal_data, output="interactive", method="timeline")

Graph Explorer (Full Dashboard)

For a full browser-based UI with search, path finding, and the Ontology Hub, launch the Explorer CLI:

semantica-explorer --graph my_graph.json

See the Explorer reference for the full feature set and REST API.

Tips and Common Pitfalls

**`plotly` is required for all visualizers.** Install before use: `pip install plotly`. All visualizer methods raise `ProcessingError` if Plotly is not installed. **Use `max_nodes` for large graphs.** Force-directed layouts become unreadable and slow above ~1,000 nodes. Filter to a subgraph before visualizing large graphs. **HTML output is always the best starting point.** Interactive HTML lets you zoom, pan, and hover for details. Only export to PNG/SVG/PDF when embedding in a report. **Use `color_scheme="colorblind"` in publications and dashboards.** The Okabe-Ito palette is readable for everyone, including the ~8% of readers who are red-green colorblind. **UMAP is faster than t-SNE at scale.** For embedding spaces with >5,000 points, UMAP completes in seconds; t-SNE may take minutes. Both produce good cluster separation. **For interactive dashboards, prefer Explorer.** `KGVisualizer.visualize_network()` generates a self-contained HTML file. The Explorer CLI (`semantica-explorer`) gives a full live web app with search, filtering, path-finding, and REST API. The graph being visualized. Visualize ontology class structure. Generate the embeddings visualized here. Full interactive Knowledge Explorer UI.