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Introduction to Complex Network Analysis with Python

Name: Introduction to Complex Network Analysis with Python
Price: 59 USD
Author: Tiago Toledo Jr

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.

5.0

32 lessons

Created by Tiago Toledo Jr

Introduction to Complex Network Analysis with Python

What's included

32 lessons across 8 modules
11 quizzes with instant feedback
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

An understanding of complex networks and how to analyze them using Python
Hands-on experience with the NetworkX Python library for complex network analysis
Familiarity with the scikit-net library to apply machine learning algorithms to complex networks
The ability to load, analyze, visualize, and infer characteristics of complex networks using Python

Learning Roadmap

32 Lessons11 Quizzes5 Assessments14 Challenges

Introduction to Graphs and Complex Networks

Get familiar with complex networks, graph types, random graphs, and NetworkX for analysis.

Introduction to the Course

Complexity and Complex Networks

Assess Your Knowledge of Graphs and Complex Networks

Assessment

Graphs and Complex Networks in Python

Get started with representing, analyzing, visualizing, and saving complex networks using Python's NetworkX library.

Network Data Representations

NetworkX Overview

Saving and Plotting Networks with NetworkX

Quiz: Graphs and Complex Networks in Python

Complex Networks Measurements

9 Lessons

Examine various centrality metrics and their applications using Python for network analysis.

Assess Your Knowledge of Complex Networks in Python

Assessment

Random Graphs and Complex Networks Models

2 Lessons

Grasp the fundamentals of small-world and scale-free network models, their properties, and generation techniques.

Community Detection in Complex Networks

4 Lessons

Take a closer look at community detection methods, including Louvain, sknet, and particle competition.

Assess Your Knowledge of Network Models and Community Detection

Assessment

Similarity in Complex Networks

2 Lessons

Tackle similarity concepts in complex networks and their use in link prediction.

Assess Your Knowledge of Similarity in Complex Networks

Assessment

Complex Network Embeddings

4 Lessons

Master the steps to generate network embeddings with DeepWalk, Node2Vec, and Graph2Vec algorithms.

Assess Your Knowledge of Network Embeddings

Assessment

Wrap Up

Get familiar with essential skills in network data manipulation, community detection, and machine learning.

Conclusion

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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.

ABOUT THE AUTHOR

Tiago Toledo Jr

Principal Data Scientist @ Media Hero. Has experience working with causal inference, complex networks, and predictive inference for large retail businesses in Brazil.

Learn more about Tiago

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