dgl.broadcast_edges¶

dgl.
broadcast_edges
(graph, graph_feat, *, etype=None)[source]¶ Generate an edge feature equal to the graphlevel feature
graph_feat
.The operation is similar to
numpy.repeat
(ortorch.repeat_interleave
). It is commonly used to normalize edge features by a global vector. For example, to normalize edge features across graph to range \([0~1)\):>>> g = dgl.batch([...]) # batch multiple graphs >>> g.edata['h'] = ... # some node features >>> h_sum = dgl.broadcast_edges(g, dgl.sum_edges(g, 'h')) >>> g.edata['h'] /= h_sum # normalize by summation
 Parameters
graph (DGLGraph) – The graph.
graph_feat (tensor) – The feature to broadcast. Tensor shape is \((*)\) for single graph, and \((B, *)\) for batched graph.
etype (str, typle of str, optional) – Edge type. Can be omitted if there is only one edge type in the graph.
 Returns
The edge features tensor with shape \((M, *)\), where \(M\) is the number of edges.
 Return type
Tensor
Examples
>>> import dgl >>> import torch as th
Create two
DGLGraph
objects and initialize their edge features.>>> g1 = dgl.graph(([0], [1])) # Graph 1 >>> g2 = dgl.graph(([0, 1], [1, 2])) # Graph 2 >>> bg = dgl.batch([g1, g2]) >>> feat = th.rand(2, 5) >>> feat tensor([[0.4325, 0.7710, 0.5541, 0.0544, 0.9368], [0.2721, 0.4629, 0.7269, 0.0724, 0.1014]])
Broadcast feature to all edges in the batched graph, feat[i] is broadcast to edges in the ith example in the batch.
>>> dgl.broadcast_edges(bg, feat) tensor([[0.4325, 0.7710, 0.5541, 0.0544, 0.9368], [0.2721, 0.4629, 0.7269, 0.0724, 0.1014], [0.2721, 0.4629, 0.7269, 0.0724, 0.1014]])
Broadcast feature to all edges in the single graph.
>>> dgl.broadcast_edges(g2, feat[1]) tensor([[0.2721, 0.4629, 0.7269, 0.0724, 0.1014], [0.2721, 0.4629, 0.7269, 0.0724, 0.1014]])
See also