dgl.DGLGraph.pull

DGLGraph.pull(v, message_func, reduce_func, apply_node_func=None, etype=None)[source]

Pull messages from the specified node(s)’ predecessors along the specified edge type, aggregate them to update the node features.

Parameters

  • v (node IDs) –

    The node IDs. The allowed formats are:

    • int: A single node.

    • Int Tensor: Each element is a node ID. The tensor must have the same device type and ID data type as the graph’s.

    • iterable[int]: Each element is a node ID.

  • message_func (dgl.function.BuiltinFunction or callable) – The message function to generate messages along the edges. It must be either a DGL Built-in Function or a User-defined Functions.

  • reduce_func (dgl.function.BuiltinFunction or callable) – The reduce function to aggregate the messages. It must be either a DGL Built-in Function or a User-defined Functions.

  • apply_node_func (callable, optional) – An optional apply function to further update the node features after the message reduction. It must be a User-defined Functions.

  • etype (str or (str, str, str), optional) –

    The type name of the edges. The allowed type name formats are:

    • (str, str, str) for source node type, edge type and destination node type.

    • or one str edge type name if the name can uniquely identify a triplet format in the graph.

    Can be omitted if the graph has only one type of edges.

Notes

  • If some of the given nodes v has no in-edges, DGL does not invoke message and reduce functions for these nodes and fill their aggregated messages with zero. Users can control the filled values via set_n_initializer(). DGL still invokes apply_node_func if provided.

  • DGL recommends using DGL’s bulit-in function for the message_func and the reduce_func arguments, because DGL will invoke efficient kernels that avoids copying node features to edge features in this case.

Examples

>>> import dgl
>>> import dgl.function as fn
>>> import torch

Homogeneous graph

>>> g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]))
>>> g.ndata['x'] = torch.ones(5, 2)
>>> g.pull([0, 3, 4], fn.copy_u('x', 'm'), fn.sum('m', 'h'))
>>> g.ndata['h']
tensor([[0., 0.],
        [0., 0.],
        [0., 0.],
        [1., 1.],
        [1., 1.]])

Heterogeneous graph

>>> g = dgl.heterograph({
...     ('user', 'follows', 'user'): ([0, 1], [1, 2]),
...     ('user', 'plays', 'game'): ([0, 2], [0, 1])
... })
>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [2.]])

Pull.

>>> g['follows'].pull(2, fn.copy_u('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
        [1.],
        [1.]])