# dgl.sum_nodes¶

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

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

Parameters: graph (DGLGraph.) – The graph. feat (str) – The feature field. weight (str, optional) – The weight field. If None, no weighting will be performed, otherwise, weight each node feature with field feat. for summation. The weight feature associated in the graph should be a tensor of shape [graph.number_of_nodes(), 1]. The summed tensor. 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 the corresponding example in the batch. If an example has no nodes, 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 node features.

>>> g1 = dgl.DGLGraph()                           # Graph 1
>>> g1.ndata['h'] = th.tensor([[1.], [2.]])
>>> g1.ndata['w'] = th.tensor([[3.], [6.]])

>>> g2 = dgl.DGLGraph()                           # Graph 2
>>> g2.ndata['h'] = th.tensor([[1.], [2.], [3.]])


Sum over node attribute h without weighting for each graph in a batched graph.

>>> bg = dgl.batch([g1, g2], node_attrs='h')
>>> dgl.sum_nodes(bg, 'h')
tensor([[3.],   # 1 + 2
[6.]])  # 1 + 2 + 3


Sum node attribute h with weight from node attribute w for a single graph.

>>> dgl.sum_nodes(g1, 'h', 'w')
tensor([15.])   # 1 * 3 + 2 * 6