import torch
from torch_sparse import coalesce
from .num_nodes import maybe_num_nodes
[docs]def is_undirected(edge_index, num_nodes=None):
r"""Returns :obj:`True` if the graph given by :attr:`edge_index` is
undirected.
Args:
edge_index (LongTensor): The edge indices.
num_nodes (int, optional): The number of nodes, *i.e.*
:obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
:rtype: bool
"""
num_nodes = maybe_num_nodes(edge_index, num_nodes)
edge_index, _ = coalesce(edge_index, None, num_nodes, num_nodes)
undirected_edge_index = to_undirected(edge_index, num_nodes=num_nodes)
return edge_index.size(1) == undirected_edge_index.size(1)
[docs]def to_undirected(edge_index, num_nodes=None):
r"""Converts the graph given by :attr:`edge_index` to an undirected graph,
so that :math:`(j,i) \in \mathcal{E}` for every edge :math:`(i,j) \in
\mathcal{E}`.
Args:
edge_index (LongTensor): The edge indices.
num_nodes (int, optional): The number of nodes, *i.e.*
:obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
:rtype: :class:`LongTensor`
"""
num_nodes = maybe_num_nodes(edge_index, num_nodes)
row, col = edge_index
row, col = torch.cat([row, col], dim=0), torch.cat([col, row], dim=0)
edge_index = torch.stack([row, col], dim=0)
edge_index, _ = coalesce(edge_index, None, num_nodes, num_nodes)
return edge_index