dgl.mean_edges¶

dgl.mean_edges(graph, feat, weight=None)[source]¶

Averages all the values of edge field feat in graph, optionally multiplies the field by a scalar edge field weight.

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 the `graph` should be a tensor of shape `[graph.number_of_edges(), 1]`.
Returns:	The averaged tensor.
Return type:	tensor

Notes

Return a stacked tensor with an extra first dimension whose size equals batch size of the input graph. The i-th row of the stacked tensor contains the readout result of the i-th graph in the batched graph. If a graph 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 attribute w 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))