This project visualizes a social media platform as a graph network, where each user is represented as a node and their connections as edges.
Using Graph Theory algorithms, the project identifies the most influential users (potential brand sponsors) and simulates how information spreads across the network.
To apply concepts of Graph Theory and Centrality Measures in analyzing social media influence and predicting optimal brand sponsorship strategies.
- 🕸️ Graph Visualization: Displays the user network using Matplotlib and NetworkX.
- 📈 Centrality Analysis: Calculates and compares:
- Degree Centrality
- Betweenness Centrality
- Eigenvector Centrality
- 🤝 Influencer Selection: Determines which influencers brands should sponsor based on network importance.
- 🔁 Influence Simulation: Models how a brand message spreads across the user base.
- ⏱️ Reach Time Calculation: Estimates how long it takes for brand awareness to reach the maximum number of users.
- 💾 Fake Data Generation: Uses Pandas and NumPy to create synthetic user and connection datasets.
- Graph Theory
- Node Centrality Measures
- Eigenvector Centrality
- Network Propagation Simulation
- Data Analysis (Pandas + NumPy)
- Graph Visualization (Matplotlib + NetworkX)
| Component | Library Used |
|---|---|
| Data Handling | Pandas, NumPy |
| Graph Operations | NetworkX |
| Visualization | Matplotlib |
| Simulation | Python (Custom Functions) |
- Clone this repository:
git clone https://github.com/your-username/social-media-influence-graph.git cd social-media-influence-graph