[CS224W] Lecture 1

1.2 Applications of Graph ML

1.2.1 Classic Graph ML Tasks

Node Classification
Link Prediction
Graph Classification (ex. Molecule property prediction)
Graph generation(drug discovery), evolution(physical simulation)
Node-level ML Tasks
Protein Folding (DeepMind’s AlphaFold)
Edge-level ML Tasks
Recommender Systems
Drug Side Effects (when taking multiple drugs)
Traffic Prediction
Graph-level ML Tasks
Drug discovery
Physics Simulation

1.2.2 Choice of Graph Representation

Directed vs Undirected Graphs
Node degrees
Bipartite graph
two different types of nodes
where nodes only interact with the other type of node
Folded/Projected Bipartite Graphs
Bipartite graph → Projection graph
Representing Graphs: Adjacency Matrix
In real-world matrix, adjacency matrices are extremely sparse
Representing Graphs: Edge list
Easier to work with large & sparse graph
Allow us to quickly retrieve all neighbors of a given node
Node and Edge Attributes
More Types of Graphs
Unweighted and Weighted
Self-edges (self-loops)
Connected undirected graph
Connected directed graph
A strongly connected directed graph
A weakly connected directed graph (disregard the edge directions)