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

Which metric can you use to find the node with most connections?

Which metric can you use to find the node with most connections?

We want to choose a city that is closer to every other city we want to visit during a trip. Which metric should we use?

We want to choose a city that is closer to every other city we want to visit during a trip. Which metric should we use?

Which type of network suffers from the zero-propagation with the eigenvector centrality?

Which type of network suffers from the zero-propagation with the eigenvector centrality?

The Katz centrality is faster to compute.

What’s the advantage of the Katz centrality over the eigenvector centrality?

What’s the advantage of the Katz centrality over the eigenvector centrality?

A node has a high Katz centrality but a low PageRank centrality. Why?

A node has a high Katz centrality but a low PageRank centrality. Why?

Which metric should we use to find the node where most information passes through in a computer router’s network?

Which metric should we use to find the node where most information passes through in a computer router’s network?

In which scenario can the PageRank centrality be reduced to the degree?

In which scenario can the PageRank centrality be reduced to the degree?

Assess your learning about complex network metrics.

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

Quiz: Complex Networks Measurement