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name: graph_structure_attention description: Build graph-derived sparse attention masks for Graphically Structured Diffusion Model style recovery experiments and checks.

Graph Structure Attention

Use this skill when a task gives a graphical model or factor graph and you need the GSDM-style attention contract: one token per variable, self-attention on every token, symmetrized variable dependencies, and optional factor-clique attention.

Do not use this skill to learn graph structure from data. The paper assumes a supplied graphical-model sketch and uses it to constrain a diffusion denoiser.

Inputs

• nodes: ordered variable ids.
• edges: directed or undirected pairs of variable ids.
• factors: optional lists of node ids that share an undirected factor.
• Optional BCMF dimensions m, n, and k for the paper's matrix-factorization graph.

Outputs

• Dense boolean mask mask.
• Packed sparse representation with attendable_indices and valid_indices_mask.
• Node metadata and simple mask statistics.

Workflow

1. Build or receive an ordered node list.
2. Add self-edges for all nodes.
3. Add graph edges in both directions.
4. For each factor scope, add all pairwise co-scope edges.
5. Pack each row of the mask to a fixed-width attendable-index array.
6. Use scripts/graph_attention.py for deterministic construction and validation.

CLI

python scripts/graph_attention.py --bcmf 2 2 2 --output /tmp/gsdm_graph.json

Validation

python -m pytest tests

The tests verify BCMF node counts, expected local dependencies, absence of unrelated edges, and round-trip reconstruction from packed sparse indices.

Limitations

The skill validates the structural mask only. It does not implement transformer layers or train a diffusion model.