dgl.DGLGraph.send_and_recv

DGLGraph.send_and_recv(edges, message_func, reduce_func, apply_node_func=None, etype=None)[source]

沿着指定的边发送消息，并在目标节点上减少它们以更新它们的特征。

Parameters:

edges (edges) –
用于发送和接收消息的边。允许的输入格式有：
- int: 单个边ID。
- Int Tensor: 每个元素都是一个边ID。张量必须具有与图相同的设备类型和ID数据类型。
- iterable[int]: 每个元素都是一个边ID。
- (Tensor, Tensor): 节点张量格式，其中两个张量的第i个元素指定一条边。
- (iterable[int], iterable[int]): 类似于节点张量格式，但将边端点存储在python可迭代对象中。
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.

注释

DGL 建议使用 DGL 的内置函数来处理 message_func 和 reduce_func 参数，因为在这种情况下，DGL 会调用高效的内核，避免将节点特征复制到边特征。

示例

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

同构图

>>> g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]))
>>> g.ndata['x'] = torch.ones(5, 2)
>>> # Specify edges using (Tensor, Tensor).
>>> g.send_and_recv(([1, 2], [2, 3]), fn.copy_u('x', 'm'), fn.sum('m', 'h'))
>>> g.ndata['h']
tensor([[0., 0.],
        [0., 0.],
        [1., 1.],
        [1., 1.],
        [0., 0.]])
>>> # Specify edges using IDs.
>>> g.send_and_recv([0, 2, 3], fn.copy_u('x', 'm'), fn.sum('m', 'h'))
>>> g.ndata['h']
tensor([[0., 0.],
        [1., 1.],
        [0., 0.],
        [1., 1.],
        [1., 1.]])

异构图

>>> g = dgl.heterograph({
...     ('user', 'follows', 'user'): ([0, 1], [1, 2]),
...     ('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1])
... })
>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [2.]])
>>> g.send_and_recv(g['follows'].edges(), fn.copy_u('h', 'm'),
...                 fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
        [0.],
        [1.]])

使用用户定义函数的``send_and_recv``

>>> import torch as th
>>> g = dgl.graph(([0, 1], [1, 2]))
>>> g.ndata['x'] = th.tensor([[1.], [2.], [3.]])

>>> # Define the function for sending node features as messages.
>>> def send_source(edges):
...     return {'m': edges.src['x']}
>>> # Sum the messages received and use this to replace the original node feature.
>>> def simple_reduce(nodes):
...     return {'x': nodes.mailbox['m'].sum(1)}

发送和接收消息。

>>> g.send_and_recv(g.edges())
>>> g.ndata['x']
tensor([[1.],
        [1.],
        [2.]])

请注意，节点0的特征保持不变，因为它没有传入边。