# torch_geometric.nn.pool.knn_graph

knn_graph(x: Tensor, k: int, batch: = None, loop: bool = False, flow: str = 'source_to_target', cosine: bool = False, num_workers: int = 1, batch_size: = None) [source]

Computes graph edges to the nearest k points.

import torch
from torch_geometric.nn import knn_graph

x = torch.Tensor([[-1, -1], [-1, 1], [1, -1], [1, 1]])
batch = torch.tensor([0, 0, 0, 0])
edge_index = knn_graph(x, k=2, batch=batch, loop=False)

Parameters
• x (torch.Tensor) – Node feature matrix $$\mathbf{X} \in \mathbb{R}^{N \times F}$$.

• k (int) – The number of neighbors.

• batch (torch.Tensor, optional) – Batch vector $$\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N$$, which assigns each node to a specific example. (default: None)

• loop (bool, optional) – If True, the graph will contain self-loops. (default: False)

• flow (str, optional) – The flow direction when using in combination with message passing ("source_to_target" or "target_to_source"). (default: "source_to_target")

• cosine (bool, optional) – If True, will use the cosine distance instead of euclidean distance to find nearest neighbors. (default: False)

• num_workers (int, optional) – Number of workers to use for computation. Has no effect in case batch is not None, or the input lies on the GPU. (default: 1)

• batch_size (int, optional) – The number of examples $$B$$. Automatically calculated if not given. (default: None)

Return type

torch.Tensor