---

name: graph-layers description: "Construct and troubleshoot the repository's sparse and dense graph layers, dynamic or dilated KNN blocks, GENConv aggregation, and reversible coupling primitives; use this skill for layer-level API and shape questions, not end-to-end dataset workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Graph layers

Use this skill for a layer-level design or debugging task. It is a distilled operating guide, not a source-checkout import recipe. Start with the tensor layout and graph representation, choose a static or dynamic layer, then run the small bundled smoke before moving to a real workload.

Route by task

• For ModelNet40, S3DIS, PartNet, point-cloud data loading, task flags, checkpoints, or visualization, hand off to the sibling point-cloud-workflows skill.
• For OGB datasets, DeeperGCN benchmark configurations, graph pooling, partitioning, or RevGNN/RevGAT experiments, hand off to ogb-workflows.
• For PPI data, F1 metrics, and PPI training/evaluation, hand off to ppi-workflows.
• Keep the root skill responsible for installation and broad routing. This skill can identify dependency failures but does not install packages or run long training.

Quick decision procedure

1. Identify layout. Use sparse node features (N, C) with a PyG edge_index (2, E) and optional batch (N,) for independent graphs; use dense point-cloud features (B, C, N, 1) with dense indices (2, B, N, K). Do not pass a dense tensor to sparse layers or flatten a dense batch without preserving graph membership.

2. Choose graph construction. Use a supplied edge_index when topology is fixed. Otherwise use DynConv/DynConv2d, selecting kernel_size K, dilation d, and (for dense layers) knn='matrix' or the compiled torch_cluster path. Ensure K*d does not exceed points per graph.

3. Choose convolution. Sparse GraphConv supports edge, mr, gat, gcn, and gin in the inspected implementation. sage and rsage are exposed but are not a supported modern-PyG route; see troubleshooting. Dense GraphConv2d and DynConv2d support edge and mr.

4. Choose composition. Plain blocks transform features, residual blocks add a same-width scaled skip, and dense blocks concatenate newly produced channels. Static blocks preserve and return edge_index; sparse dynamic blocks return (features, batch).

5. For generalized aggregation, configure GENConv and validate the aggregator, temperature/power parameters, edge encoding, and message normalization together. See aggregation and blocks.

6. For memory-efficient depth, use a channel-divisible group additive coupling with deterministic per-group functions, then wrap it with the reversible wrapper only after a direct forward/inverse round trip passes. See reversible.

7. Run the safe helper from any working directory:

   python /absolute/path/to/graph-layers/scripts/layer_smoke.py --help
   python /absolute/path/to/graph-layers/scripts/layer_smoke.py --tiny
   

   Resolve the absolute path in the caller's skill installation; never add a source checkout to PYTHONPATH for this helper.

Dependency boundary

The core layer behavior depends on a coherent PyTorch, PyTorch Geometric, torch-scatter, and torch-cluster installation. The verified inspection combination was PyTorch 2.11.0+cu128, PyG 2.8.0.post1, torch-scatter 2.1.2+pt211cu128, and torch-cluster 1.6.3+pt211cu128, with pip check passing. Treat those versions as evidence, not as a universal pin: match PyG extension wheels to the installed PyTorch and CUDA/CPU build. A missing or ABI-incoherent compiled extension is a dependency failure, not a layer-shape bug. The tiny helper probes these dependencies without importing repository modules.

Exact benchmark reproduction is intentionally outside this skill. The repository-era PyG 1.6.3 probe did not import with torch 1.13.1 because of the removed torch._six.container_abcs; use a coherent historical environment for old-number reproduction and do not infer benchmark equivalence from the modern smoke.

References

• API and shape reference
• Aggregation and block choices
• Reversible coupling and wrapper contracts
• Troubleshooting