Explore complex network theory, metrics, and analysis via Python's NetworkX. Gain insights into creating, visualizing, and applying networks in fields like machine learning and data analysis.

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Complex networks are a powerful data structure capable of modeling several complex data relationships such as social media interactions, ecosystems, energy systems, and financial information.

In this course, you’ll learn the basic theory behind complex networks and the metrics commonly used to analyze them. Then, you’ll learn how to create complex networks, visualize them, and calculate their analysis metrics using NetworkX, the most used Python library for complex network analysis. Finally, you’ll learn some advanced applications of complex networks, such as machine learning applications and embedding generation using complex networks.

By the end of this course, you’ll have a basic understanding of what complex networks are and how you can use them in your application using Python. With this knowledge, you can generate more advanced analyses and examine data from new perspectives, improving your analytical skills.

Introduction to Complex Network Analysis with Python

C = \frac{\text{Closed triplets}}{\text{All triplets}}.

C = \frac{3 \times\text{ Number of triangles}}{{\text{All triplets}}}.

Learn about the clustering coefficient metric and how to assess network connectivity and clustering.

Introduction to Graphs and Complex Networks

Assess Your Knowledge of Graphs and Complex Networks

Graphs and Complex Networks in Python

Complex Networks Measurements

Assess Your Knowledge of Complex Networks in Python

Random Graphs and Complex Networks Models

Community Detection in Complex Networks

Assess Your Knowledge of Network Models and Community Detection

Similarity in Complex Networks

Assess Your Knowledge of Similarity in Complex Networks

Complex Network Embeddings

Assess Your Knowledge of Network Embeddings

Wrap Up

The Clustering Coefficient

The global clustering coefficient: Transitivity