BSc IT Project Guide: Network Graph Visualization
1. Project Title
Network Graph Visualization for Social Networks
2. Objective
To develop a tool that visualizes complex network connections such as social networks, using interactive graph structures to represent relationships and data flow between nodes.
3. Scope of the Project
- Import and parse network data (CSV, JSON, or database)
- Visualization of nodes and edges
- Zoom, pan, and drag interactions
- Display node/edge properties on click/hover
- Dynamic graph updates
- Export or share visualizations
4. Technology Stack
Frontend: HTML, CSS, JavaScript, D3.js / Cytoscape.js / vis.js
Backend: Python (Flask/Django) or Node.js (optional)
Database: MongoDB / Neo4j / JSON Files
IDE: VS Code / PyCharm
Visualization Libraries: D3.js, NetworkX, Plotly
5. System Modules
Data Import Module: Load and format graph data
Graph Rendering Module: Display nodes and edges
Interaction Module: Handle zoom, pan, drag, click
Filter/Search Module: Highlight nodes by attribute
Export Module: Save graphs as image or JSON
6. ER Diagram Overview
- Node(NodeID, Label, Type, Attributes)
- Edge(EdgeID, SourceNodeID, TargetNodeID, RelationType, Weight)
7. Sample Data Format (JSON)
{
"nodes": [
{"id": "1", "label": "Alice"},
{"id": "2", "label": "Bob"}
],
"edges": [
{"source": "1", "target": "2", "label": "friend"}
]
}
8. Functional Requirements
- Import data from JSON/CSV
- Display network graph with visual styling
- Node interaction (hover/click info)
- Graph filtering and search
- Export visualization
9. Non-Functional Requirements
- Lightweight and responsive UI
- Support for large-scale networks
- Cross-browser compatibility
- Accessible design and color coding
10. Development Roadmap
Phase 1: Requirement analysis and dataset collection
Phase 2: Design and selection of graph library
Phase 3: Development of data parser and visualization modules
Phase 4: Integration, testing, optimization
Phase 5: Final documentation
11. Suggested Project Report Structure
1. Title Page
2. Acknowledgement
3. Certificate
4. Abstract
5. Table of Contents
6. Introduction
7. System Analysis
8. System Design
9. Implementation
10. Testing
11. Limitations & Future Enhancements
12. Conclusion
13. References
14. Appendix
12. Optional Enhancements
- Real-time data updates using WebSocket
- Integration with Neo4j for dynamic querying
- Node clustering and community detection
- Heatmaps and influence scores
- 3D graph visualization using Three.js or Plotly