tfgnn.Adjacency

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Stores how edges connect pairs of nodes from source and target node sets.

Inherits From: HyperAdjacency

tfgnn.Adjacency(
    data: Data, spec: 'GraphPieceSpecBase'
)

Each hyper-edge connect one node from the source node set with one node from the target node sets. The source and target node sets could be the same. The adjacency information is a pair of integer tensors containing indices of nodes in source and target node sets. Those tensors are indexed by edges, have the same type spec and shape of [*graph_shape, num_edges], where num_edges is the number of edges in the edge set (could be potentially ragged). The index tensors are of tf.Tensor type if num_edges is not None or graph_shape.rank = 0 and oftf.RaggedTensor type otherwise.

The Adjacency is a composite tensor and a special case of tfgnn.HyperAdjacency class with tfgnn.SOURCE and tfgnn.TARGET node tags used for the source and target nodes correspondingly.

Args
data	Nest of Field or subclasses of GraphPieceBase.
spec	A subclass of GraphPieceSpecBase with a _data_spec that matches data.

Attributes
indices_dtype	The dtype for graph items indexing. One of tf.int32 or tf.int64.
rank	The rank of this Tensor. Guaranteed not to be None.
row_splits_dtype	The dtype for ragged row partitions. One of tf.int32 or tf.int64.
shape	A possibly-partial shape specification for this Tensor. The returned tf.TensorShape is guaranteed to have a known rank and no unknown dimensions except possibly the outermost.
source	The indices of source nodes.
source_name	The node set name of source nodes.
spec	The public type specification of this tensor.
target	The indices of target nodes.
target_name	The node set name of target nodes.

Methods

from_indices

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@classmethod
from_indices(
    source: tfgnn.Field,
    target: tfgnn.Field,
    *_,
    validate: Optional[bool] = None
) -> 'Adjacency'

Constructs a new instance from the source and target node indices.

Example 1:

# Single graph (rank is 0). Connects pairs of nodes (a[0], b[2]),
# (a[1], b[1]), (a[2], b[0]) from node sets a and b.
tfgnn.Adjacency.from_indices(('a', [0, 1, 2]),
                             ('b', [2, 1, 0]))

Example 2:

# Batch of two graphs (rank is 1). Connects pairs of nodes in
# graph 0: (a[0], b[2]), (a[1], b[1]); graph 1: (a[2], b[0]).
tfgnn.Adjacency.from_indices(('a', tf.ragged.constant([[0, 1], [2]])),
                             ('b', tf.ragged.constant([[2, 1], [0]])))

Args
source	The tuple of node set name and nodes index integer tensor. The index must have shape of [*graph_shape, num_edges], where num_edges is the number of edges in each graph (could be ragged). It has tf.Tensor type if num_edges is not None or graph_shape.rank = 0 and tf.RaggedTensor type otherwise.
target	Like source field, but for target edge endpoint. Index tensor must have the same type spec as for the source.
validate	If True, checks that source and target indices have the same type spec.

Returns
An Adjacency tensor with a shape and an indices_dtype being inferred from the indices values.

get_indices_dict

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get_indices_dict() -> Dict[IncidentNodeTag, Tuple[NodeSetName, Field]]

Returns copy of indices as a dictionary.

node_set_name

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node_set_name(
    node_set_tag: IncidentNodeTag
) -> NodeSetName

Returns a node set name for the given node set tag.

set_shape

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set_shape(
    new_shape: ShapeLike
) -> 'GraphPieceBase'

Deprecated. Use with_shape().

with_indices_dtype

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with_indices_dtype(
    dtype: tf.dtypes.DType
) -> 'GraphPieceBase'

Returns a copy of this piece with the given indices dtype.

with_row_splits_dtype

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with_row_splits_dtype(
    dtype: tf.dtypes.DType
) -> 'GraphPieceBase'

Returns a copy of this piece with the given row splits dtype.

with_shape

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with_shape(
    new_shape: ShapeLike
) -> 'GraphPieceBase'

Enforce the common prefix shape on all the contained features.

__getitem__

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__getitem__(
    node_set_tag: IncidentNodeTag
) -> tfgnn.Field

Returns an index tensor for the given node set tag.