dgl.DGLGraph.update_all

DGLGraph.update_all(message_func, reduce_func, apply_node_func=None, etype=None)

Send messages along all the edges of the specified type and update all the nodes of the corresponding destination type.

For heterogeneous graphs with number of relation types > 1, send messages along all the edges, reduce them by type-wisely and across different types at the same time. Then, update the node features of all the nodes.

Parameters:

• 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 nodes in the graph 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.update_all(fn.copy_u('x', 'm'), fn.sum('m', 'h'))
>>> g.ndata['h']
tensor([[0., 0.],
        [1., 1.],
        [1., 1.],
        [1., 1.],
        [1., 1.]])

Heterogeneous graph

>>> g = dgl.heterograph({('user', 'follows', 'user'): ([0, 1, 2], [1, 2, 2])})

Update all.

>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [2.]])
>>> g['follows'].update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
        [0.],
        [3.]])

Heterogenenous graph (number relation types > 1)

>>> g = dgl.heterograph({
...     ('user', 'follows', 'user'): ([0, 1], [1, 1]),
...     ('game', 'attracts', 'user'): ([0], [1])
... })

Update all.

>>> g.nodes['user'].data['h'] = torch.tensor([[1.], [2.]])
>>> g.nodes['game'].data['h'] = torch.tensor([[1.]])
>>> g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
>>> g.nodes['user'].data['h']
tensor([[0.],
        [4.]])