Knowledge Graph Visualization.md - himent12/FlashGenie GitHub Wiki
๐ Knowledge Graph Visualization
FlashGenie's Knowledge Graph Visualization creates interactive visual representations of your learning progress and concept relationships.
๐ฏ 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
๐ก 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
๐ 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 |
๐ 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
๐ 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)
๐งช 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
๐ฏ Tips for Using Knowledge Graphs
- Focus on weak clusters - Areas with low mastery scores need attention
- Follow prerequisite paths - Master foundational concepts first
- Identify central concepts - High-centrality nodes connect many ideas
- Look for isolated concepts - Disconnected nodes may need integration
- Track mastery progression - Watch the graph evolve as you learn
๐ Next Steps
- Adaptive Study Sessions - Create optimized study plans
- Learning Velocity Tracking - Track your learning progress
- Content Recommendations - Get AI-suggested content
FlashGenie v1.5.0 | Last updated: June 2025