This introductory course provides a detailed and lucid explanation of various concepts in Graph Machine Learning, along with coding examples wherever necessary.

networkx.tar.gz

Networkx

Are you ready to attain mastery in graph machine learning? Graphs are ubiquitous and have diverse applications in various fields. In this introductory course, you will learn the fundamentals of graph machine learning so that you’re able to work with different types of graphs, state-of-the-art graph machine learning techniques, and various graph analytics tasks.

The course begins with the basics of graphs and gradually progresses to more advanced topics, including graph embedding and its different techniques and different use cases built on knowledge graphs. 

By the end of the course, you will have gained hands-on experience in creating and manipulating graphs, as well as a comprehensive understanding of knowledge graphs and graph neural networks. This course will equip you with the skills and knowledge to step into advanced graph machine learning topics for research or practical applications and take your career to the next level.

Introduction to Graph Machine Learning

**(Select all that apply.)** Which of the following can representation learning capture or learn?

(Select all that apply.) Which of the following can representation learning capture or learn?

**(True or False)** Eigenvalue decomposition is one way to generate graph embeddings.

(True or False) Eigenvalue decomposition is one way to generate graph embeddings.

**(True or False)** The embedding algorithm automatically computes the dimension (or length) of a node embedding during the training process.

(True or False) The embedding algorithm automatically computes the dimension (or length) of a node embedding during the training process.

**(Select all that apply.)** What does the `DeepWalk` algorithm take its inspiration from?

(Select all that apply.) What does the <code>DeepWalk</code> algorithm take its inspiration from?

**(Select all that apply.)** Which of the following is true about the SDNE algorithm? 

(Select all that apply.) Which of the following is true about the SDNE algorithm?

Test your understanding of graph embeddings.

About the Course

Introduction to Graph Theory

Graph Embeddings

Supervised and Unsupervised Graph ML

Graph Neural Networks

Knowledge Graph

Knowledge Graph Embeddings

Case Study: Link Prediction on a Social Network Graph

Case Study: Node Classification on a Biological Graph

Appendix

Quiz: Graph Embeddings