# UniformNegativeSamplerο

class dgl.graphbolt.UniformNegativeSampler(datapipe, graph, negative_ratio)[source]ο

Sample negative destination nodes for each source node based on a uniform distribution.

Functional name: `sample_uniform_negative`.

Itβs important to note that the term βnegativeβ refers to false negatives, indicating that the sampled pairs are not ensured to be absent in the graph. For each edge `(u, v)`, it is supposed to generate negative_ratio pairs of negative edges `(u, v')`, where `v'` is chosen uniformly from all the nodes in the graph.

Parameters:
• datapipe (DataPipe) β The datapipe.

• graph (FusedCSCSamplingGraph) β The graph on which to perform negative sampling.

• negative_ratio (int) β The proportion of negative samples to positive samples.

Examples

```>>> from dgl import graphbolt as gb
>>> indptr = torch.LongTensor([0, 1, 2, 3, 4])
>>> indices = torch.LongTensor([1, 2, 3, 0])
>>> graph = gb.fused_csc_sampling_graph(indptr, indices)
>>> node_pairs = torch.tensor([[0, 1], [1, 2], [2, 3], [3, 0]])
>>> item_set = gb.ItemSet(node_pairs, names="node_pairs")
>>> item_sampler = gb.ItemSampler(
...     item_set, batch_size=4,)
>>> neg_sampler = gb.UniformNegativeSampler(
...     item_sampler, graph, 2)
>>> for minibatch in neg_sampler:
...       print(minibatch.negative_srcs)
...       print(minibatch.negative_dsts)
None
tensor([[2, 1],
[2, 1],
[3, 2],
[1, 3]])
```