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name: ppi-workflows description: "Operate the DeepGCNs PPI node-classification workflow with exact train/test flags, residual/plain/dense architecture choices, PyG loader and DataParallel boundaries, micro/macro F1 semantics, and checkpoint alignment without downloading data or running full training." disable-model-invocation: true metadata: disco-role: operating license: MIT

PPI workflows

Use this skill for the source-evidenced protein-protein interaction (PPI) node classification workflow, including its residual/plain/dense backbones and F1-based evaluation. This is a reference-only operating skill. The original source example is evidence only, not a runtime dependency: use only a caller-supplied PPI entry point and staged local inputs. Do not copy or launch the full training program, download PPI data, fetch external-drive checkpoints, or claim that a CPU smoke reproduces the published GPU result.

Route first

• PPI node classification: read workflows.md, then validate the data and metric contract in data-and-metrics.md.
• Generic GraphConv, EdgeConv, MRConv, GAT/GIN/GCN/SAGE construction, KNN, GENConv, or block API questions: route to the sibling graph-layers skill. Keep this skill's architecture choices at the task level.
• OGB proteins or another OGB benchmark: route to ogb-workflows. OGB proteins is not the PyG PPI dataset and does not share its split, feature, label, or checkpoint conventions.
• ModelNet40, S3DIS, PartNet, or point-cloud layouts: route to the sibling point-cloud workflow skill.

Safe operating sequence

1. Establish the requested phase (train or test), prepared local data root, feature and label dimensions, device/backend, loader mode, architecture tuple (block, conv, n_filters, n_blocks, and n_heads), and checkpoint path if any.
2. Verify that the data and checkpoint were staged by the caller. The upstream PyG dataset can auto-download when files are absent, but this skill never permits that side effect. Do not run a download or embed a downloader.
3. Check the exact flags and source-observed defaults in workflows.md. In particular, do not rely on the README's prose default for filter width: the parser's default is authoritative for the source workflow.
4. Align the checkpoint basename and tensor shapes with the requested model before loading. A checkpoint is not interchangeable merely because it is labelled ppi.
5. Prefer a tiny, local, synthetic forward/parser check when validating an environment. Treat full PPI training, validation, and benchmark numbers as GPU/data/checkpoint-dependent prerequisites, not as bundled actions.
6. Report micro F1 separately from macro F1. The source path computes micro F1 only; it does not produce macro F1.

Operating invariants

• PPI examples use static sparse graph convolutions over PyG edge_index. Node features have shape [num_nodes, num_features]; output logits have shape [num_nodes, num_classes]. The model obtains data.batch, but the static PPI forward path does not use it to construct a graph.
• The standard PyG PPI contract is 50 floating-point node features and 121 binary gene-ontology labels. n_classes is inferred from the test dataset; it is not a CLI flag.
• The loss is BCEWithLogitsLoss. A positive prediction is formed by testing the raw logit with out > 0, equivalent to a sigmoid threshold of 0.5.
• The documented score is micro F1 (average="micro") over multilabel node predictions. Macro F1 (average="macro") is a different statistic and must not be inferred from the source's mF1/m-F1 log label.
• Checkpoint architecture identity is ppi-{block}-{conv}-{n_blocks}-{n_filters}, optionally followed by -{postname}. The phase suffix is _val_best or _test_best; see the checkpoint section in the references for the save-directory date component.
• --multi_gpus selects PyG DataListLoader for training and wraps the model with PyG DataParallel. The source constructs ordinary DataLoader objects for validation/test, so multi-GPU evaluation is a compatibility boundary and must be checked rather than assumed.

Scope boundary

The references preserve exact source-observed commands, flags, data fields, metric behavior, checkpoint naming, and failure modes without bundling the training implementation. No PPI configuration checker is included: the parser has source-era boolean and path behavior, while a safe checker that faithfully validates data/checkpoint contents would either duplicate the training stack or invite data access. This explicit reference-only choice keeps the runtime tree offline-safe and makes the unsupported parts visible.