---

name: graph-data-and-datasets description: "Use CogDL graph data objects, custom datasets, built-in dataset selection, masks, batching, and no-download data validation." disable-model-invocation: true metadata: disco-role: operating license: MIT

CogDL Graph Data and Datasets

Use this sub-skill when the task is about CogDL data objects rather than model architecture or training orchestration.

Use when

• Creating or inspecting cogdl.data.Graph, Adjacency, Dataset, NodeDataset, GraphDataset, generate_random_graph, or DataLoader.
• Building a no-download custom node-classification or graph-classification fixture.
• Choosing a built-in dataset name while making download/cache/dependency effects explicit.
• Debugging edge_index, CSR fields, masks, labels, node features, subgraphs, local_graph, self-loops, normalization, or batched graph objects.

Route elsewhere

• Training loops, wrapper matching, checkpoints, loggers, or Trainer details: ../training-wrappers-and-customization/SKILL.md.
• High-level experiment() calls, CLI flags, variants, or AutoML: ../experiments-and-cli/SKILL.md.
• Model, layer, sparse operator, PyG/Jittor/DGL, or CUDA acceleration details: ../models-layers-and-operators/SKILL.md.
• Application pipelines such as OAG-BERT, recommendation, visualization, or embedding pipelines: ../pipelines-and-applications/SKILL.md.

Operating checklist

1. Prefer a self-contained Graph or custom dataset artifact before loading a built-in dataset. Built-in datasets may download data and write caches when missing.
2. Validate node-classification fixtures before handing them to experiments or wrappers. Use the bundled scripts/validate_graph_masks.py on a saved Graph or NodeDataset artifact.
3. For quick no-download examples, use scripts/create_tiny_graph_dataset.py --output-dir <directory> to create deterministic node and graph classification artifacts.
4. Keep Graph schemas explicit: tensor x, tensor y, COO edge_index, and boolean or index masks with node-count-compatible lengths.
5. For graph classification, save a list of local-node-indexed Graph objects and batch them with cogdl.data.DataLoader; if node features are absent, route the degree-feature training decision to the training-wrapper sub-skill.

Reference map

• references/graph-and-dataset-api.md: verified public data APIs and graph methods.
• references/data-formats.md: COO/CSR formats, masks, batching, mutation semantics, and validation checklist.
• references/built-in-datasets.md: dataset families, registered names, and network/cache/dependency caveats.
• references/troubleshooting.md: common failures and recovery steps for graph data construction.