---

name: graph-apis description: "Construct, inspect, transform, batch, and debug homogeneous and heterogeneous DGL graphs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DGL Graph APIs

Use this sub-skill when a task needs to create, inspect, transform, batch, or debug DGL homogeneous or heterogeneous graphs before model training or data loading.

Start Here

• For constructors, object relationships, feature views, idtype/device rules, conversions, blocks, batching, and subgraph APIs, read references/api-reference.md.
• For compact recipes, including homogeneous graphs, heterographs, canonical edge types, transforms, batching, and validation checks, read references/workflows.md.
• For failure diagnosis around canonical edge-type ambiguity, feature schema mismatches, device/idtype errors, zero-node assumptions, batching, and import/native-library errors, read references/troubleshooting.md.
• To verify a local DGL install can run the graph basics safely, run python scripts/graph_smoke.py from this sub-skill directory.

Route Elsewhere

• CSV ingestion, DGLDataset, CSVDataset, graph caching, and persistent save/load depth belong in datasets-and-io.
• Neighbor sampling, minibatch DataLoader, and GraphBolt pipelines belong in dataloading-graphbolt.
• Message passing, update_all, built-in functions, GNN layers, and training loops belong in message-passing-training.
• Distributed partitioning, launchers, and cluster/runtime tools belong in distributed-tools.