dmlc--dgl
Quan (Andy) Gan
63 行
2.9 KiB
ReStructuredText
63 行
2.9 KiB
ReStructuredText
.. _guide-graph-feature:
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1.3 Node and Edge Features
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--------------------------
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The nodes and edges of a :class:`~dgl.DGLGraph` can have several user-defined named features for
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storing graph-specific properties of the nodes and edges. These features can be accessed
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via the :py:attr:`~dgl.DGLGraph.ndata` and :py:attr:`~dgl.DGLGraph.edata` interface. For example, the following code creates two node
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features (named ``'x'`` and ``'y'`` in lines 8 and 15) and one edge feature (named ``'x'`` in line 9).
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.. code-block:: python
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:linenos:
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>>> import dgl
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>>> import torch as th
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>>> g = dgl.graph(([0, 0, 1, 5], [1, 2, 2, 0])) # 6 nodes, 4 edges
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>>> g
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Graph(num_nodes=6, num_edges=4,
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ndata_schemes={}
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edata_schemes={})
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>>> g.ndata['x'] = th.ones(g.num_nodes(), 3) # node feature of length 3
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>>> g.edata['x'] = th.ones(g.num_edges(), dtype=th.int32) # scalar integer feature
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>>> g
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Graph(num_nodes=6, num_edges=4,
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ndata_schemes={'x' : Scheme(shape=(3,), dtype=torch.float32)}
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edata_schemes={'x' : Scheme(shape=(,), dtype=torch.int32)})
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>>> # different names can have different shapes
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>>> g.ndata['y'] = th.randn(g.num_nodes(), 5)
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>>> g.ndata['x'][1] # get node 1's feature
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tensor([1., 1., 1.])
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>>> g.edata['x'][th.tensor([0, 3])] # get features of edge 0 and 3
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tensor([1, 1], dtype=torch.int32)
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Important facts about the :py:attr:`~dgl.DGLGraph.ndata`/:py:attr:`~dgl.DGLGraph.edata` interface:
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- Only features of numerical types (e.g., float, double, and int) are allowed. They can
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be scalars, vectors or multi-dimensional tensors.
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- Each node feature has a unique name and each edge feature has a unique name.
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The features of nodes and edges can have the same name. (e.g., 'x' in the above example).
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- A feature is created via tensor assignment, which assigns a feature to each
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node/edge in the graph. The leading dimension of that tensor must be equal to the
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number of nodes/edges in the graph. You cannot assign a feature to a subset of the
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nodes/edges in the graph.
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- Features of the same name must have the same dimensionality and data type.
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- The feature tensor is in row-major layout -- each row-slice stores the feature of one
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node or edge (e.g., see lines 10-11 in the above example).
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For weighted graphs, one can store the weights as an edge feature as below.
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.. code-block:: python
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>>> # edges 0->1, 0->2, 0->3, 1->3
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>>> edges = th.tensor([0, 0, 0, 1]), th.tensor([1, 2, 3, 3])
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>>> weights = th.tensor([0.1, 0.6, 0.9, 0.7]) # weight of each edge
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>>> g = dgl.graph(edges)
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>>> g.edata['w'] = weights # give it a name 'w'
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>>> g
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Graph(num_nodes=4, num_edges=4,
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ndata_schemes={}
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edata_schemes={'w' : Scheme(shape=(,), dtype=torch.float32)})
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See APIs: :py:attr:`~dgl.DGLGraph.ndata`, :py:attr:`~dgl.DGLGraph.edata`.
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