Knowledge Graph Visualization.md - himent12/FlashGenie GitHub Wiki

๐Ÿ” Knowledge Graph Visualization

FlashGenie's Knowledge Graph Visualization creates interactive visual representations of your learning progress and concept relationships.

๐ŸŽฏ What is Knowledge Graph Visualization?

Knowledge Graph Visualization is an interactive tool that:

  • Maps connections between concepts you're learning
  • Visualizes mastery levels across your knowledge domains
  • Identifies knowledge gaps and prerequisite relationships
  • Tracks learning progress through visual indicators
  • Suggests learning paths based on concept dependencies

๐Ÿ’ก Key Features

Interactive Concept Maps

# Generate a basic knowledge graph
python -m flashgenie graph "Biology"

The knowledge graph shows:

  • Nodes - Individual concepts/cards
  • Edges - Relationships between concepts
  • Colors - Mastery level indicators
  • Size - Concept importance/centrality

Mastery Heatmaps

# Generate a mastery heatmap
python -m flashgenie graph "Spanish" --type heatmap

Heatmaps provide:

  • Color-coded mastery levels
  • Clustered topic areas
  • Progress indicators
  • Recommended focus areas

Dependency Trees

# Generate a concept dependency tree
python -m flashgenie graph "Math" --type dependency

Dependency trees show:

  • Prerequisite relationships
  • Learning sequence recommendations
  • Mastery bottlenecks
  • Critical path concepts

๐Ÿš€ Using Knowledge Graph Visualization

Basic Usage

# Generate and view a knowledge graph
python -m flashgenie graph "Spanish Vocabulary"

Advanced Usage

# Generate a detailed knowledge graph with export
python -m flashgenie graph "Biology" \
  --type network \
  --depth 3 \
  --include-tags \
  --highlight-struggling \
  --export biology_graph.html

Available Parameters

Parameter Description Example
--type Visualization type --type network (network, heatmap, dependency, radial)
--depth Relationship depth --depth 2 (1-5)
--include-tags Show tag relationships --include-tags
--highlight Highlight specific areas --highlight-struggling (struggling, mastered, new)
--export Export visualization --export graph.html

๐Ÿ“Š Visualization Types

Network Graph

python -m flashgenie graph "Physics" --type network

Best for:

  • Seeing overall concept relationships
  • Identifying central concepts
  • Understanding knowledge clusters

Radial Graph

python -m flashgenie graph "History" --type radial

Best for:

  • Focusing on a central concept
  • Seeing hierarchical relationships
  • Visualizing concept distance

Sunburst Chart

python -m flashgenie graph "Programming" --type sunburst

Best for:

  • Hierarchical category visualization
  • Proportional mastery representation
  • Topic/subtopic relationships

๐Ÿ” Analyzing Your Knowledge Graph

Identifying Knowledge Gaps

# Find knowledge gaps
python -m flashgenie graph-analysis "Math" --find-gaps

Sample output:

Knowledge Gap Analysis:
- Major gap detected: "Differential Equations" (prerequisite for 5 concepts)
- Missing connection: "Linear Algebra" โ†’ "Machine Learning"
- Weak area: "Statistics" cluster (avg. mastery: 42%)

Learning Path Recommendations

# Get optimal learning path
python -m flashgenie graph-analysis "Programming" --learning-path

Sample output:

Recommended Learning Path:
1. "Basic Syntax" (mastered)
2. "Control Structures" (mastered)
3. "Functions" (in progress - 72%)
4. "Data Structures" (recommended next focus)
5. "Object-Oriented Programming" (prerequisite for later concepts)

๐Ÿงช The Science Behind It

Knowledge Graph Visualization is based on:

  • Concept Mapping Theory - Visual representation of knowledge structures
  • Graph Theory - Mathematical structures to model relationships
  • Knowledge Space Theory - Modeling knowledge acquisition paths
  • Semantic Networks - Representing meaning through connected concepts
  • Learning Analytics - Data-driven insights into learning patterns

๐ŸŽฏ Tips for Using Knowledge Graphs

  1. Focus on weak clusters - Areas with low mastery scores need attention
  2. Follow prerequisite paths - Master foundational concepts first
  3. Identify central concepts - High-centrality nodes connect many ideas
  4. Look for isolated concepts - Disconnected nodes may need integration
  5. Track mastery progression - Watch the graph evolve as you learn

๐Ÿš€ Next Steps


FlashGenie v1.5.0 | Last updated: June 2025