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name: ogb-workflows description: "Run and adapt the repository's OGB node, graph, and link benchmark workflows with DeeperGCN/GENConv, partitioned or batched data, feature encoders, reversible proteins models, and the optional DGL RevGAT teacher/student path. Use this skill for OGB task configuration, checkpoint contracts, memory boundaries, and safe synthetic validation; route generic layer APIs to graph-layers, PPI to ppi-workflows, and point-cloud tasks to point-cloud-workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

OGB workflows

Use this skill when the request names OGB, ogbn-*, ogbg-*, ogbl-collab, DeeperGCN, GENConv, RevGNN, RevGAT, or graph knowledge distillation. It is an operating guide for the documented workflows, not a promise to download data or reproduce a large benchmark in the current session.

Route first

• Node classification: ogbn-arxiv is full-batch; ogbn-products uses random induced partitions; ogbn-proteins uses partitioning and edge-derived initial features. Use ogb-tasks.md.
• Graph property prediction: ogbg-molhiv, ogbg-molpcba, and ogbg-ppa use PyG graph batches, categorical/edge feature handling, and graph pooling.
• Link prediction: ogbl-collab uses a full node encoder plus a batched MLP link predictor and Hits@10/50/100 evaluation.
• Memory-efficient proteins: use reversible-ogb.md only when the user explicitly needs the RevGCN backbone or depth/memory tradeoff.
• DGL RevGAT: optional and separately gated; it needs a DGL-compatible CUDA environment. Do not substitute it for the PyG GENConv workflows.
• Route a request about a layer constructor, aggregation implementation, KNN, or generic reversible primitive to graph-layers. Route PPI to ppi-workflows and point-cloud datasets to point-cloud-workflows.

Safe operating sequence

1. Confirm dataset, task type, metric, device, available cached data, and checkpoint availability. A CPU model smoke does not establish large-graph memory or benchmark accuracy.
2. Start with the task-specific defaults and change only the flags needed by the requested comparison. The task parsers use conv=gen; the live GENConv API defaults are documented in the references and are not always identical to task-parser defaults.
3. Prepare the data without implicit network access. OGB constructors may download when data is absent; stop and ask for an approved, already-cached dataset location rather than allowing a bundled helper to fetch it.
4. Use the command shape only from the task reference. Native task modules use directory-local imports; an independently staged implementation must provide an equivalent task-local entrypoint rather than relying on arbitrary dotted imports.
5. Save and load the complete checkpoint expected by the task. Check the model and predictor separately for ogbl-collab; use model_state_dict from the repository's standard training checkpoints. Never infer a result from a checkpoint whose architecture flags differ.
6. For a bounded check, run scripts/ogb_model_smoke.py --help and then its tiny synthetic validation. It never creates a dataset, accesses a network, or loads a checkpoint.

GENConv and block choices

The task models use sparse GENConv inside plain, res, or res+ blocks; dense is advertised by some parsers but the OGB model implementations raise NotImplementedError. The important knobs are --gcn_aggr, --t, --p, --y, --learn_t, --learn_p, --learn_y, --msg_norm, --learn_msg_scale, --norm, and --mlp_layers. Edge-aware molecular and protein models additionally use --conv_encode_edge; molecular models use --add_virtual_node and --graph_pooling.

Do not silently carry generic layer assumptions into a benchmark workflow: use graph-layers for constructor-level semantics, shape debugging, or unsupported convolution alternatives. See the four bundled references for exact task flags, data layouts, checkpoints, reversible memory, and failure diagnosis.

Evidence boundary

Numbers printed in the README-derived tables are documented benchmark results, tied to the repository's historical PyTorch/PyG environment and external pretrained files. They are not results verified by this skill. The bundled helper provides only a verified tiny synthetic smoke when it passes; it does not validate OGB splits, ROC-AUC/accuracy/Hits, GPU memory, or training quality. The selected inspection environment had PyTorch 2.11.0+cu128, PyG 2.8.0.post1, matching scatter/cluster extensions, and OGB 1.3.6; DGL was not selected. Modern PyG also has a known incompatibility in the repository's SAGE wrapper; do not present that route as supported.