torch_geometric.transforms.AddLaplacianEigenvectorPE

class AddLaplacianEigenvectorPE(k: int, attr_name: Optional[str] = 'laplacian_eigenvector_pe', is_undirected: bool = False, **kwargs)[source]

Bases: BaseTransform

Adds the Laplacian eigenvector positional encoding from the “Benchmarking Graph Neural Networks” paper to the given graph (functional name: add_laplacian_eigenvector_pe).

Parameters

  • k (int) – The number of non-trivial eigenvectors to consider.

  • attr_name (str, optional) – The attribute name of the data object to add positional encodings to. If set to None, will be concatenated to data.x. (default: "laplacian_eigenvector_pe")

  • is_undirected (bool, optional) – If set to True, this transform expects undirected graphs as input, and can hence speed up the computation of eigenvectors. (default: False)

  • **kwargs (optional) – Additional arguments of scipy.sparse.linalg.eigs() (when is_undirected is False) or scipy.sparse.linalg.eigsh() (when is_undirected is True).