Admin
Joined - December 2025
Key Features:
Modern GUI Design:
Clean, professional interface with multiple panels
Tabbed interface for different views
Status bar and progress indicators
Responsive layout
Network Visualization:
Multiple layout algorithms (Spring, Circular, Kamada-Kawai, Spectral)
Interactive zoom and pan
Node coloring by influence or community
Edge weight visualization
Analysis Capabilities:
Basic network metrics (density, clustering coefficient, etc.)
Centrality analysis (degree, betweenness, closeness, PageRank)
Community detection using Louvain algorithm
Path analysis between nodes
Influencer identification
Data Management:
Import/export CSV, JSON, GEXF, GraphML
Sample data generation
Node filtering by degree
Data table view with sortable columns
Advanced Features:
Multi-threading for long operations
Color customization
Network statistics window
Comprehensive documentation
Required Packages:
bash
pip install networkx matplotlib pandas numpy scipy scikit-learn python-louvain pillow seaborn
Usage Instructions:
Run the application:
bash
python social_network_analyzer.py
Load sample data or import your own
Use the visualization tab to view the network
Perform analyses from the Analysis menu or Quick Actions panel
Export results as needed
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