dgl.topk_nodes

dgl.topk_nodes(graph, feat, k, *, descending=True, sortby=None, ntype=None)[source]

通过在graph中的节点特征feat上按索引sortby的特征进行图级别的top-k操作，返回图级别的表示。

If descending is set to False, return the k smallest elements instead.

如果 sortby 设置为 None，该函数将在所有维度上独立执行 top-k 操作，相当于调用 torch.topk(graph.ndata[feat], dim=0)。

Parameters:

graph (DGLGraph) – The graph.
feat (str) – The feature field.
k (int) – The k in “top-k”
descending (bool) – Controls whether to return the largest or smallest elements.
sortby (int, optional) – Sort according to which feature. If is None, all features are sorted independently.
ntype (str, optional) – Node type. Can be omitted if there is only one node type in the graph.

Returns:

sorted_feat (Tensor) – A tensor with shape \((B, K, D)\), where \(B\) is the batch size of the input graph.
sorted_idx (Tensor) – A tensor with shape \((B, K)`(:math:`(B, K, D)\) if sortby is set to None), where \(B\) is the batch size of the input graph, \(D\) is the feature size.

注释

If an example has \(n\) nodes and \(n<k\), the sorted_feat tensor will pad the \(n+1\) to \(k\) th rows with zero;

示例

>>> import dgl
>>> import torch as th

Create two DGLGraph objects and initialize their node features.

>>> g1 = dgl.graph(([0, 1], [2, 3]))              # Graph 1
>>> g1.ndata['h'] = th.rand(4, 5)
>>> g1.ndata['h']
tensor([[0.0297, 0.8307, 0.9140, 0.6702, 0.3346],
        [0.5901, 0.3030, 0.9280, 0.6893, 0.7997],
        [0.0880, 0.6515, 0.4451, 0.7507, 0.5297],
        [0.5171, 0.6379, 0.2695, 0.8954, 0.5197]])

>>> g2 = dgl.graph(([0, 1, 2], [2, 3, 4]))       # Graph 2
>>> g2.ndata['h'] = th.rand(5, 5)
>>> g2.ndata['h']
tensor([[0.3168, 0.3174, 0.5303, 0.0804, 0.3808],
        [0.1323, 0.2766, 0.4318, 0.6114, 0.1458],
        [0.1752, 0.9105, 0.5692, 0.8489, 0.0539],
        [0.1931, 0.4954, 0.3455, 0.3934, 0.0857],
        [0.5065, 0.5182, 0.5418, 0.1520, 0.3872]])

在批处理图中，基于节点属性 h 的Top-k。

>>> bg = dgl.batch([g1, g2], ndata=['h'])
>>> dgl.topk_nodes(bg, 'h', 3)
(tensor([[[0.5901, 0.8307, 0.9280, 0.8954, 0.7997],
          [0.5171, 0.6515, 0.9140, 0.7507, 0.5297],
          [0.0880, 0.6379, 0.4451, 0.6893, 0.5197]],
         [[0.5065, 0.9105, 0.5692, 0.8489, 0.3872],
          [0.3168, 0.5182, 0.5418, 0.6114, 0.3808],
          [0.1931, 0.4954, 0.5303, 0.3934, 0.1458]]]), tensor([[[1, 0, 1, 3, 1],
          [3, 2, 0, 2, 2],
          [2, 3, 2, 1, 3]],
         [[4, 2, 2, 2, 4],
          [0, 4, 4, 1, 0],
          [3, 3, 0, 3, 1]]]))

在批处理图中沿最后一个维度对节点属性 h 进行Top-k操作。（用于SortPooling）

>>> dgl.topk_nodes(bg, 'h', 3, sortby=-1)
(tensor([[[0.5901, 0.3030, 0.9280, 0.6893, 0.7997],
          [0.0880, 0.6515, 0.4451, 0.7507, 0.5297],
          [0.5171, 0.6379, 0.2695, 0.8954, 0.5197]],
         [[0.5065, 0.5182, 0.5418, 0.1520, 0.3872],
          [0.3168, 0.3174, 0.5303, 0.0804, 0.3808],
          [0.1323, 0.2766, 0.4318, 0.6114, 0.1458]]]), tensor([[1, 2, 3],
         [4, 0, 1]]))

在单个图中基于节点属性 h 的Top-k。

>>> dgl.topk_nodes(g1, 'h', 3)
(tensor([[[0.5901, 0.8307, 0.9280, 0.8954, 0.7997],
          [0.5171, 0.6515, 0.9140, 0.7507, 0.5297],
          [0.0880, 0.6379, 0.4451, 0.6893, 0.5197]]]), tensor([[[1, 0, 1, 3, 1],
          [3, 2, 0, 2, 2],
          [2, 3, 2, 1, 3]]]))