Introduction to the applications and impact of graph machine learning across various fields.

• The lecture will cover different levels of machine learning tasks including node, edge, subgraph, and graph-level tasks.
• Explains the significance of graph machine learning in real-world applications.

Discussion of node-level tasks such as node classification and link prediction, with protein folding as a key example.

• Node classification for categorizing online users or items.
• Link prediction used in knowledge graph completion.
• Graph-level tasks include categorizing entire graphs and predicting properties of molecules for drug design.
• Introduction to the problem of protein folding solved by DeepMind's AlphaFold using graph neural networks.

Illustrates edge-level tasks with examples from recommender systems and drug side-effect prediction.

• Recommender systems use bipartite graphs to predict user interests and rely on graph neural networks for improved recommendations.
• Drug side-effect prediction utilizes a heterogeneous network to predict adverse interactions between drugs.

Covers subgraph-level tasks using traffic prediction in Google Maps as an example.

• Traffic prediction represents road segments as nodes and connectivity as edges, using graph neural networks to predict travel times.

Explores graph-level tasks with a focus on drug discovery and physics-based simulations.

• Drug discovery using graph-based deep learning for antibiotic discovery and molecule classification.
• Physics-based simulations predict material deformation by simulating particle movement and interactions.