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name: graph-data description: "Create, validate, batch, slice, and convert Graph Nets graph data with GraphsTuple, data dictionaries, NumPy arrays, and NetworkX graphs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Graph Data

Use this sub-skill when a task needs to build or inspect Graph Nets data before it reaches TensorFlow or Sonnet modules. It covers graphs.GraphsTuple, per-graph data dictionaries, NumPy arrays, NetworkX conversion, batching, unbatching, and slicing.

Read first

• Data formats: field semantics, valid None combinations, batch offsets, data dictionary rules, and NetworkX feature conventions.
• API reference: exact NumPy conversion and slicing functions, expected inputs/outputs, and common preflight checks.
• Troubleshooting: fast diagnosis for key-set, endpoint, feature, and NetworkX compatibility failures.
• Smoke script: deterministic round-trip check that prints JSON and can be run from any working directory.

Typical workflows

1. Create a GraphsTuple directly when arrays are already in batched form. Provide all seven fields: nodes, edges, receivers, senders, globals, n_node, and n_edge; keep n_node and n_edge non-None.
2. Create from per-graph data dictionaries when each sample is easier to describe independently. Use utils_np.data_dicts_to_graphs_tuple, and make every dictionary have the same non-None field set.
3. Create from NetworkX when graph topology is naturally object-based. Use utils_np.networkx_to_data_dict for one graph or utils_np.networkxs_to_graphs_tuple for a batch; enforce ordered integer node keys and the "features" attribute convention.
4. Round-trip or inspect a batch with utils_np.graphs_tuple_to_data_dicts, utils_np.graphs_tuple_to_networkxs, or utils_np.get_graph.
5. Use GraphsTuple.replace or GraphsTuple.map for structural-preserving field updates; invalid None combinations still raise immediately.

Boundary routing

• TensorFlow placeholders, tensor batching, padding, specs, tf.Session handling, and utils_tf workflows belong to tensorflow-ops.
• Sonnet blocks, graph network modules, learned architecture choices, and model call requirements belong to graph-models.
• Long notebook-style training recipes belong to the root demo recipes and, for model construction, graph-models.

Minimum preflight checklist

• Confirm whether the graph is featureful or featureless separately for nodes, edges, and globals.
• Confirm receivers and senders are both present or both absent; featureless edges still need endpoints.
• Confirm n_node and n_edge are present for manually constructed GraphsTuple objects and for featureless/empty data dictionaries that cannot infer counts from arrays.
• When batching, confirm all data dictionaries have identical non-None key sets, including number fields if any dictionary provides them.
• When using NetworkX, confirm the runtime has networkx.OrderedMultiDiGraph or another compatible networkx<3 graph class accepted by the conversion path.