Method add_nodes

python/dgl/heterograph.py:283–433

r"""Add new nodes of the same node type Parameters ---------- num : int Number of nodes to add. data : dict, optional Feature data of the added nodes. ntype : str, optional The type of the new nodes. Can be omitted if there

(self, num, data=None, ntype=None)

    #################################################################

    def add_nodes(self, num, data=None, ntype=None):
        r"""Add new nodes of the same node type

        Parameters
        ----------
        num : int
            Number of nodes to add.
        data : dict, optional
            Feature data of the added nodes.
        ntype : str, optional
            The type of the new nodes. Can be omitted if there is
            only one node type in the graph.

        Notes
        -----

        * Inplace update is applied to the current graph.
        * If the key of ``data`` does not contain some existing feature fields,
          those features for the new nodes will be created by initializers
          defined with :func:`set_n_initializer` (default initializer fills zeros).
        * If the key of ``data`` contains new feature fields, those features for
          the old nodes will be created by initializers defined with
          :func:`set_n_initializer` (default initializer fills zeros).
        * This function discards the batch information. Please use
          :func:`dgl.DGLGraph.set_batch_num_nodes`
          and :func:`dgl.DGLGraph.set_batch_num_edges` on the transformed graph
          to maintain the information.

        Examples
        --------

        The following example uses PyTorch backend.

        >>> import dgl
        >>> import torch

        **Homogeneous Graphs or Heterogeneous Graphs with A Single Node Type**

        >>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
        >>> g.num_nodes()
        3
        >>> g.add_nodes(2)
        >>> g.num_nodes()
        5

        If the graph has some node features and new nodes are added without
        features, their features will be created by initializers defined
        with :func:`set_n_initializer`.

        >>> g.ndata['h'] = torch.ones(5, 1)
        >>> g.add_nodes(1)
        >>> g.ndata['h']
        tensor([[1.], [1.], [1.], [1.], [1.], [0.]])

        We can also assign features for the new nodes in adding new nodes.

        >>> g.add_nodes(1, {'h': torch.ones(1, 1), 'w': torch.ones(1, 1)})
        >>> g.ndata['h']