Apply the Graph Machine Learning curriculum to a checked project.
Project brief
Required concepts
- Graph learning contract: entities, relations and snapshot time
- Graph construction: direction, duplicates and high-degree nodes
- Neighborhood features: aggregate only the graph that existed at prediction time
- Link prediction evaluation: time splits and honest negative candidates
- Graph model evaluation: report sparse-node and relation-specific failures
- Graph serving: handle new nodes, edge deletion and snapshot swaps
Completion standard
Submit runnable work, a data manifest, measured results and failure-case evidence.