Graph Theory for Machine Learning
Nodes, edges, adjacency matrix, graph types, graph features, social network representations.
What is a Graph
A graph models entities and the relationships between them. Formally a graph is G = (V, E) where V is the set of vertices (nodes) and E is the set of edges connecting pairs of nodes. Many real systems are naturally graphs.
Nodes and Edges
Each node represents an entity (a user, an atom, a web page). Each edge represents a relationship (a friendship, a chemical bond, a hyperlink). Graph machine learning learns from this connective structure, not just isolated samples.
All lessons in this course
- Graph Theory for Machine Learning
- Graph Convolutional Networks (GCN)
- Node Classification with GNN
- Link Prediction and Graph Classification