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

Graph neural networks have taken inspiration from which method?

Graph neural networks have taken inspiration from which method?

**(True or False)** Node feature aggregation is more important than selecting the initial node features in graph neural networks.

(True or False) Node feature aggregation is more important than selecting the initial node features in graph neural networks.

What are some common applications of GNNs?


What are some common applications of GNNs?

They use basis functions like eigenvectors in the Laplacian matrix.

They use a fixed set of filters that can be directly applied to the graph.

Which one of the following statements is true about spatial-based GNNs?

Which one of the following statements is true about spatial-based GNNs?

Which function is used in the neural message passing phase of a GNN?

Which function is used in the neural message passing phase of a GNN?

Test your understanding of graph neural networks.

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 Neural Networks