📊 Social Network Analysis Course Repository

This repository contains materials, assignments, and projects from my Social Network Analysis course as a fourth-year Data Science student.

📚 Table of Contents

• Introduction
• Repository Structure
• Technologies Used
• Installation & Usage
• Course Content
• Contributing
• License
• Contact

🌟 Introduction

This repository showcases various aspects of Social Network Analysis, including:

• Network structure analysis
• Key metrics calculation (PageRank, HITS, Centrality)
• Community detection
• Text analysis and topic modeling
• Real-world SNA applications

The code and materials here demonstrate both theoretical understanding and practical implementation of SNA concepts.

📂 Repository Structure

social-network-analysis/
│
├── data/                    # Sample data and datasets
├── notebooks/              # Jupyter notebooks for analysis
├── src/                    # Source code
│   ├── page_rank_hits_demo.py
│   ├── tf_idf_lda_demo.py
│   └── overview.py
├── docs/                   # Course materials
│   └── 01. Introduction to Social Networks.pdf
├── results/                # Analysis outputs
├── requirements.txt        # Project dependencies
└── README.md

🔧 Technologies Used

• Core Technologies:

  • Python 3.8+
  • NetworkX for graph analysis
  • Scikit-learn for machine learning
  • Pandas for data manipulation
  • NumPy for numerical computations
  • Matplotlib/Plotly for visualization

• Key Libraries:

import networkx as nx
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import LatentDirichletAllocation

💻 Installation & Usage

1. Clone the repository:

git clone https://github.com/your-username/social-network-analysis.git
cd social-network-analysis

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

3. Install dependencies:

pip install -r requirements.txt

📖 Course Content

1. Network Fundamentals

• Graph theory basics
• Network types and properties
• Social network characteristics
• Data collection methods

2. Network Analysis Techniques

• Centrality Measures:

  • Degree centrality
  • Betweenness centrality
  • Closeness centrality
  • Eigenvector centrality

• Ranking Algorithms:

  • PageRank
  • HITS (Hyperlink-Induced Topic Search)
  • Preferential Attachment

3. Community Detection

• Louvain method
• Girvan-Newman algorithm
• Modularity optimization
• Clique percolation

4. Text Analysis in Social Networks

• TF-IDF implementation
• Topic modeling with LDA
• Sentiment analysis
• Network text visualization

5. Practical Applications

• Friend network analysis
• Influence measurement
• Information diffusion
• Recommendation systems

🔍 Example Code

PageRank and HITS Implementation

import networkx as nx

# Create sample graph
G = nx.DiGraph()
G.add_edges_from([("A", "B"), ("B", "C"), ("C", "A")])

# Calculate PageRank
pagerank = nx.pagerank(G, alpha=0.85)
print("PageRank:", pagerank)

# Calculate HITS
hubs, authorities = nx.hits(G)
print("Hubs:", hubs)
print("Authorities:", authorities)

🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more information.

📫 Contact