Source code for torch_geometric.transforms.line_graph

import torch
from torch import Tensor

from import Data
from import functional_transform
from torch_geometric.transforms import BaseTransform
from torch_geometric.utils import coalesce, cumsum, remove_self_loops, scatter

[docs]@functional_transform('line_graph') class LineGraph(BaseTransform): r"""Converts a graph to its corresponding line-graph (functional name: :obj:`line_graph`). .. math:: L(\mathcal{G}) &= (\mathcal{V}^{\prime}, \mathcal{E}^{\prime}) \mathcal{V}^{\prime} &= \mathcal{E} \mathcal{E}^{\prime} &= \{ (e_1, e_2) : e_1 \cap e_2 \neq \emptyset \} Line-graph node indices are equal to indices in the original graph's coalesced :obj:`edge_index`. For undirected graphs, the maximum line-graph node index is :obj:`(data.edge_index.size(1) // 2) - 1`. New node features are given by old edge attributes. For undirected graphs, edge attributes for reciprocal edges :obj:`(row, col)` and :obj:`(col, row)` get summed together. Args: force_directed (bool, optional): If set to :obj:`True`, the graph will be always treated as a directed graph. (default: :obj:`False`) """ def __init__(self, force_directed: bool = False) -> None: self.force_directed = force_directed def forward(self, data: Data) -> Data: assert data.edge_index is not None edge_index, edge_attr = data.edge_index, data.edge_attr N = data.num_nodes edge_index, edge_attr = coalesce(edge_index, edge_attr, num_nodes=N) row, col = edge_index if self.force_directed or data.is_directed(): i = torch.arange(row.size(0), dtype=torch.long, device=row.device) count = scatter(torch.ones_like(row), row, dim=0, dim_size=data.num_nodes, reduce='sum') ptr = cumsum(count) cols = [i[ptr[col[j]]:ptr[col[j] + 1]] for j in range(col.size(0))] rows = [row.new_full((c.numel(), ), j) for j, c in enumerate(cols)] row, col =, dim=0),, dim=0) data.edge_index = torch.stack([row, col], dim=0) data.x = data.edge_attr data.num_nodes = edge_index.size(1) else: # Compute node indices. mask = row < col row, col = row[mask], col[mask] i = torch.arange(row.size(0), dtype=torch.long, device=row.device) (row, col), i = coalesce( torch.stack([[row, col], dim=0),[col, row], dim=0) ], dim=0),[i, i], dim=0), N, ) # Compute new edge indices according to `i`. count = scatter(torch.ones_like(row), row, dim=0, dim_size=data.num_nodes, reduce='sum') joints = list(torch.split(i, count.tolist())) def generate_grid(x: Tensor) -> Tensor: row = x.view(-1, 1).repeat(1, x.numel()).view(-1) col = x.repeat(x.numel()) return torch.stack([row, col], dim=0) joints = [generate_grid(joint) for joint in joints] joint =, dim=1) joint, _ = remove_self_loops(joint) N = row.size(0) // 2 joint = coalesce(joint, num_nodes=N) if edge_attr is not None: data.x = scatter(edge_attr, i, dim=0, dim_size=N, reduce='sum') data.edge_index = joint data.num_nodes = edge_index.size(1) // 2 data.edge_attr = None return data