dgl.DGLGraph.prop_nodes¶
-
DGLGraph.
prop_nodes
(nodes_generator, message_func, reduce_func, apply_node_func=None, etype=None)[source]¶ Propagate messages using graph traversal by sequentially triggering
pull()
on nodes.The traversal order is specified by the
nodes_generator
. It generates node frontiers, which is a list or a tensor of nodes. The nodes in the same frontier will be triggered together, while nodes in different frontiers will be triggered according to the generating order.- Parameters
nodes_generator (iterable[node IDs]) – The generator of node frontiers. Each frontier is a set of node IDs stored in Tensor or python iterables. It specifies which nodes perform
pull()
at each step.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.
Examples
>>> import torch >>> import dgl >>> import dgl.function as fn
Instantiate a heterogrph and perform multiple rounds of message passing.
>>> g = dgl.heterograph({('user', 'follows', 'user'): ([0, 1, 2, 3], [2, 3, 4, 4])}) >>> g.nodes['user'].data['h'] = torch.tensor([[1.], [2.], [3.], [4.], [5.]]) >>> g['follows'].prop_nodes([[2, 3], [4]], fn.copy_u('h', 'm'), ... fn.sum('m', 'h'), etype='follows') tensor([[1.], [2.], [1.], [2.], [3.]])
See also