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https://github.com/raj-tyagi/social-network-dynamic-analysis
A comprehensive project leveraging Social Network Analysis (SNA) techniques to explore relationships, identify key influencers, detect communities, and predict future connections within a dummy social network
https://github.com/raj-tyagi/social-network-dynamic-analysis
Last synced: 9 days ago
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A comprehensive project leveraging Social Network Analysis (SNA) techniques to explore relationships, identify key influencers, detect communities, and predict future connections within a dummy social network
- Host: GitHub
- URL: https://github.com/raj-tyagi/social-network-dynamic-analysis
- Owner: raj-tyagi
- License: mit
- Created: 2024-11-30T14:07:40.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-12-17T14:39:22.000Z (about 2 months ago)
- Last Synced: 2024-12-17T15:39:31.764Z (about 2 months ago)
- Language: Jupyter Notebook
- Size: 11.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Social-Network-Dynamic-Analysis
A comprehensive project leveraging Social Network Analysis (SNA) techniques to explore relationships, identify key influencers, detect communities, and predict future connections within a dummy social network
This repository contains the implementation and analysis of a **Social Network Analysis (SNA)** project, which studies the structure, dynamics, and key factors influencing relationships within a dummy social network. The analysis employs various SNA techniques and visualizations to derive insights into the network's behavior and structure.
---
## 📜 **Introduction**
Social Network Analysis involves studying the relationships between nodes (individuals, groups, organizations, etc.) in a network. This project analyzes a synthetic dataset modeling interactions between individuals, aiming to:
- Understand network dynamics.
- Identify influential individuals.
- Detect communities.
- Explore demographic-based interaction patterns.
- Predict potential future connections.
---
## 🧑💻 **Features**
1. **Network Construction:**
- Nodes: Attributes such as gender, section, and unique identifiers.
- Edges: Represent relationships between individuals.
- Tools: NetworkX for graph modeling, Matplotlib for visualization.
2. **Visualization:**
- Spring and Circular layouts to display network structures.
- Node and edge attributes to differentiate demographics and centrality.
3. **Centrality Analysis:**
- Degree, Betweenness, Closeness, and Eigenvector Centrality to identify key individuals in the network.
4. **Community Detection:**
- Modular community detection for subgroup analysis.
5. **Gender-Based Interaction Analysis:**
- Homophily trends in Male-Male, Female-Female, and Male-Female relationships.
6. **Link Prediction:**
- Adamic-Adar index to forecast potential future relationships.
7. **Information Diffusion:**
- Simulated processes to study how information spreads through the network.
---
## 🛠️ **Tools and Technologies**
- **Languages:** Python
- **Libraries:** NetworkX, Matplotlib, Pandas
- **Graph Techniques:** Centrality measures, Modularity Maximization, Link Prediction
- **Visualization:** Advanced node and edge styling for effective data representation
---
## 📈 **Key Insights**
1. Influential nodes identified based on centrality measures.
2. Distinct communities and tightly-knit groups revealed.
3. Gender-based patterns influence relationships and interactions.
4. Central nodes play a critical role in information dissemination.
5. Link prediction suggests areas for potential network expansion.
---
## 📊 **Project Structure**
```
📂 Social-Network-Analysis
├── 📁 data # Contains the dummy dataset
├── 📁 visualizations # Generated network visualizations
├── 📂 notebooks # Jupyter Notebook for the implementation
├── 📄 requirements.txt # Python dependencies
└── 📄 README.md # Project documentation
```
---
## 🚀 **Getting Started**
1. Clone the repository:
```bash
git clone https://github.com//Social-Network-Analysis.git
```
2. Navigate to the project directory:
```bash
cd Social-Network-Analysis
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Jupyter Notebook:
```bash
jupyter notebook
```
---
## 📚 **Use Cases**
- **Education:** Identifying isolated students or groups needing attention.
- **Healthcare:** Studying the spread of information in medical networks.
- **Marketing:** Finding key influencers to target audiences effectively.
- **Team Management:** Optimizing group structures.
- **Political Networks:** Analyzing alliances and interactions.
---
## 📝 **Future Enhancements**
- Incorporating real-world, dynamic datasets.
- Implementing advanced machine learning algorithms for link prediction.
- Expanding demographic attributes for deeper insights.
---
## 🤝 **Contributing**
Contributions are welcome! Please follow these steps:
1. Fork the repository.
2. Create a new branch: `git checkout -b feature-name`.
3. Commit your changes: `git commit -m "Add feature-name"`.
4. Push to the branch: `git push origin feature-name`.
5. Submit a pull request.
---
## 📧 **Contact**
For queries or contributions, feel free to reach out:
- **Raj Tyagi**: [LinkedIn](https://www.linkedin.com/in/raj-tyagi-83765b21b/)