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Introduction to Complex Network Analysis with Python
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
4h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
1.
Introduction to Graphs and Complex Networks
Introduction to Graphs and Complex Networks
Get familiar with complex networks, graph types, random graphs, and NetworkX for analysis.
2.
Graphs and Complex Networks in Python
Graphs and Complex Networks in Python
Get started with representing, analyzing, visualizing, and saving complex networks using Python's NetworkX library.
3.
Complex Networks Measurements
Complex Networks Measurements
9 Lessons
9 Lessons
Examine various centrality metrics and their applications using Python for network analysis.
4.
Random Graphs and Complex Networks Models
Random Graphs and Complex Networks Models
2 Lessons
2 Lessons
Grasp the fundamentals of small-world and scale-free network models, their properties, and generation techniques.
5.
Community Detection in Complex Networks
Community Detection in Complex Networks
4 Lessons
4 Lessons
Take a closer look at community detection methods, including Louvain, sknet, and particle competition.
6.
Similarity in Complex Networks
Similarity in Complex Networks
2 Lessons
2 Lessons
Tackle similarity concepts in complex networks and their use in link prediction.
7.
Complex Network Embeddings
Complex Network Embeddings
4 Lessons
4 Lessons
Master the steps to generate network embeddings with DeepWalk, Node2Vec, and Graph2Vec algorithms.
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Developed by MAANG Engineers
ABOUT 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.
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