topobench.transforms.liftings.graph2hypergraph.knn_lifting module#

This module implements the HypergraphKNNLifting class.

class Graph2HypergraphLifting(**kwargs)#

Bases: GraphLifting

Abstract class for lifting graphs to hypergraphs.

Parameters:
**kwargsoptional

Additional arguments for the class.

__init__(**kwargs)#
class HypergraphKNNLifting(k_value=1, loop=True, **kwargs)#

Bases: Graph2HypergraphLifting

Lift graphs to hypergraph domain by considering k-nearest neighbors.

Parameters:
k_valueint, optional

The number of nearest neighbors to consider. Must be positive. Default is 1.

loopbool, optional

If True the hyperedges will contain the node they were created from.

**kwargsoptional

Additional arguments for the class.

Raises:
ValueError

If k_value is less than 1.

TypeError

If k_value is not an integer or if loop is not a boolean.

__init__(k_value=1, loop=True, **kwargs)#
lift_topology(data)#

Lift a graph to hypergraph by considering k-nearest neighbors.

Parameters:
datatorch_geometric.data.Data

The input data to be lifted.

Returns:
dict

The lifted topology.

knn_graph(x, k, batch=None, loop=False, flow='source_to_target', cosine=False, num_workers=1, batch_size=None)#

Computes graph edges to the nearest k points.

Parameters:

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

  • k (int) – The number of neighbors.

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

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

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

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

  • num_workers (int) – 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:

LongTensor

import torch
from torch_cluster 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)