Welcome to the Network Analysis with Python documentation! This guide explores how to analyze complex networks using Python libraries like NetworkX and Matplotlib. Whether you're studying social networks, biological systems, or infrastructure, Python offers powerful tools for network modeling and visualization.

📚 What is Network Analysis?

Network analysis is the study of relationships between entities. In Python, you can:

  • Model networks as graphs
  • Calculate metrics like centrality and clustering
  • Visualize network structures
  • Analyze community patterns

For example, you might analyze:

  • Social media connections 📱
  • Protein interactions in cells 🧬
  • Transportation systems 🚗

🛠️ Key Python Libraries

Here are essential libraries for network analysis:

  1. NetworkX

    NetworkX

    A versatile library for creating and analyzing complex networks.

  2. Matplotlib

    Matplotlib

    Used for visualizing network diagrams and graphs.

  3. Gephi (🔗 Explore visualization tools)
    A desktop tool for interactive network analysis.

🧭 Step-by-Step Guide

  1. Install dependencies 

    pip install networkx matplotlib

  2. Create a network 

    import networkx as nx
G = nx.Graph()
G.add_edges_from([(1,2), (2,3), (3,4), (4,1)])

  3. Visualize the network 

    nx.draw(G, with_labels=True)

  4. Analyze metrics 

    nx.centrality.betweenness_centrality(G)

📌 Practical Examples

  • Social Network Analysis: Identify key influencers in a graph.
    Social_Network
  • Web Graphs: Analyze link structures between websites.
  • Biological Networks: Study gene interactions in organisms.

🔍 Expand Your Knowledge

For deeper insights, check these resources:

Let me know if you need help with specific projects or code examples! 🚀