dgl.mean_edges¶

dgl.
mean_edges
(graph, feat, weight=None)[source]¶ Averages all the values of edge field
feat
ingraph
, optionally multiplies the field by a scalar edge fieldweight
.Parameters:  graph (DGLGraph) – The graph.
 feat (str) – The feature field.
 weight (optional, str) – The weight field. If None, no weighting will be performed,
otherwise, weight each edge feature with field
feat
. for calculating mean. The weight feature associated in thegraph
should be a tensor of shape[graph.number_of_edges(), 1]
.
Returns: The averaged tensor.
Return type: tensor
Notes
If graph is a
BatchedDGLGraph
object, a stacked tensor is returned instead, i.e. having an extra first dimension. Each row of the stacked tensor contains the readout result of corresponding example in the batch. If an example has no edges, a zero tensor with the same shape is returned at the corresponding row.Examples
>>> import dgl >>> import torch as th
Create two
DGLGraph
objects and initialize their edge features.>>> g1 = dgl.DGLGraph() # Graph 1 >>> g1.add_nodes(2) >>> g1.add_edges([0, 1], [1, 0]) >>> g1.edata['h'] = th.tensor([[1.], [2.]]) >>> g1.edata['w'] = th.tensor([[3.], [6.]])
>>> g2 = dgl.DGLGraph() # Graph 2 >>> g2.add_nodes(3) >>> g2.add_edges([0, 1, 2], [1, 2, 0]) >>> g2.edata['h'] = th.tensor([[1.], [2.], [3.]])
Average over edge attribute
h
without weighting for each graph in a batched graph.>>> bg = dgl.batch([g1, g2], edge_attrs='h') >>> dgl.mean_edges(bg, 'h') tensor([[1.5000], # (1 + 2) / 2 [2.0000]]) # (1 + 2 + 3) / 3
Sum edge attribute
h
with normalized weight from edge attributew
for a single graph.>>> dgl.mean_edges(g1, 'h', 'w') # h1 * (w1 / (w1 + w2)) + h2 * (w2 / (w1 + w2)) tensor([1.6667]) # 1 * (3 / (3 + 6)) + 2 * (6 / (3 + 6))
See also