/docs/source/guide/graph-feature.rst

.. _guide-graph-feature:

1.3 Node and Edge Features
--------------------------

:ref:`(中文版)<guide_cn-graph-feature>`

The nodes and edges of a :class:`~dgl.DGLGraph` can have several user-defined named features for
storing graph-specific properties of the nodes and edges. These features can be accessed
via the :py:attr:`~dgl.DGLGraph.ndata` and :py:attr:`~dgl.DGLGraph.edata` interface. For example, the following code creates two node
features (named ``'x'`` and ``'y'`` in line 5 and 8) and one edge feature (named ``'x'`` in line 6).

.. code-block:: python
    :linenos:

    >>> import dgl
    >>> import torch as th
    >>> g = dgl.graph(([0, 0, 1, 5], [1, 2, 2, 0])) # 6 nodes, 4 edges
    >>> g
    Graph(num_nodes=6, num_edges=4,
          ndata_schemes={}
          edata_schemes={})
    >>> g.ndata['x'] = th.ones(g.num_nodes(), 3)               # node feature of length 3
    >>> g.edata['x'] = th.ones(g.num_edges(), dtype=th.int32)  # scalar integer feature
    >>> g
    Graph(num_nodes=6, num_edges=4,
          ndata_schemes={'x' : Scheme(shape=(3,), dtype=torch.float32)}
          edata_schemes={'x' : Scheme(shape=(,), dtype=torch.int32)})
    >>> # different names can have different shapes
    >>> g.ndata['y'] = th.randn(g.num_nodes(), 5)
    >>> g.ndata['x'][1]                  # get node 1's feature
    tensor([1., 1., 1.])
    >>> g.edata['x'][th.tensor([0, 3])]  # get features of edge 0 and 3
        tensor([1, 1], dtype=torch.int32)

Important facts about the :py:attr:`~dgl.DGLGraph.ndata`/:py:attr:`~dgl.DGLGraph.edata` interface:

- Only features of numerical types (e.g., float, double, and int) are allowed. They can
  be scalars, vectors or multi-dimensional tensors.
- Each node feature has a unique name and each edge feature has a unique name.
  The features of nodes and edges can have the same name. (e.g., 'x' in the above example).
- A feature is created via tensor assignment, which assigns a feature to each
  node/edge in the graph. The leading dimension of that tensor must be equal to the
  number of nodes/edges in the graph. You cannot assign a feature to a subset of the
  nodes/edges in the graph.
- Features of the same name must have the same dimensionality and data type.
- The feature tensor is in row-major layout -- each row-slice stores the feature of one
  node or edge (e.g., see lines 10-11 in the above example).

For weighted graphs, one can store the weights as an edge feature as below.

.. code-block:: python

    >>> # edges 0->1, 0->2, 0->3, 1->3
    >>> edges = th.tensor([0, 0, 0, 1]), th.tensor([1, 2, 3, 3])
    >>> weights = th.tensor([0.1, 0.6, 0.9, 0.7])  # weight of each edge
    >>> g = dgl.graph(edges)
    >>> g.edata['w'] = weights  # give it a name 'w'
    >>> g
    Graph(num_nodes=4, num_edges=4,
          ndata_schemes={}
          edata_schemes={'w' : Scheme(shape=(,), dtype=torch.float32)})

See APIs: :py:attr:`~dgl.DGLGraph.ndata`, :py:attr:`~dgl.DGLGraph.edata`.