Source code for torch_geometric.transforms.add_metapaths

from typing import List

import torch
from torch_sparse import SparseTensor

from import HeteroData
from import functional_transform
from torch_geometric.transforms import BaseTransform
from torch_geometric.typing import EdgeType

[docs]@functional_transform('add_metapaths') class AddMetaPaths(BaseTransform): r""" Adds additional edge types to a :class:`` object between the source node type and the destination node type of a given :obj:`metapath`, as described in the `"Heterogenous Graph Attention Networks" <>`_ paper (functional name: :obj:`add_metapaths`). Meta-path based neighbors can exploit different aspects of structure information in heterogeneous graphs. Formally, a metapath is a path of the form .. math:: \mathcal{V}_1 \xrightarrow{R_1} \mathcal{V}_2 \xrightarrow{R_2} \ldots \xrightarrow{R_{\ell-1}} \mathcal{V}_{\ell} in which :math:`\mathcal{V}_i` represents node types, and :math:`R_j` represents the edge type connecting two node types. The added edge type is given by the sequential multiplication of adjacency matrices along the metapath, and is added to the :class:`` object as edge type :obj:`(src_node_type, "metapath_*", dst_node_type)`, where :obj:`src_node_type` and :obj:`dst_node_type` denote :math:`\mathcal{V}_1` and :math:`\mathcal{V}_{\ell}`, repectively. In addition, a :obj:`metapath_dict` object is added to the :class:`` object which maps the metapath-based edge type to its original metapath. .. code-block:: python from torch_geometric.datasets import DBLP from import HeteroData from torch_geometric.transforms import AddMetaPaths data = DBLP(root)[0] # 4 node types: "paper", "author", "conference", and "term" # 6 edge types: ("paper","author"), ("author", "paper"), # ("paper, "term"), ("paper", "conference"), # ("term, "paper"), ("conference", "paper") # Add two metapaths: # 1. From "paper" to "paper" through "conference" # 2. From "author" to "conference" through "paper" metapaths = [[("paper", "conference"), ("conference", "paper")], [("author", "paper"), ("paper", "conference")]] data = AddMetaPaths(metapaths)(data) print(data.edge_types) >>> [("author", "to", "paper"), ("paper", "to", "author"), ("paper", "to", "term"), ("paper", "to", "conference"), ("term", "to", "paper"), ("conference", "to", "paper"), ("paper", "metapath_0", "paper"), ("author", "metapath_1", "conference")] print(data.metapath_dict) >>> {("paper", "metapath_0", "paper"): [("paper", "conference"), ("conference", "paper")], ("author", "metapath_1", "conference"): [("author", "paper"), ("paper", "conference")]} Args: metapaths (List[List[Tuple[str, str, str]]]): The metapaths described by a list of lists of :obj:`(src_node_type, rel_type, dst_node_type)` tuples. drop_orig_edges (bool, optional): If set to :obj:`True`, existing edge types will be dropped. (default: :obj:`False`) keep_same_node_type (bool, optional): If set to :obj:`True`, existing edge types between the same node type are not dropped even in case :obj:`drop_orig_edges` is set to :obj:`True`. (default: :obj:`False`) drop_unconnected_nodes (bool, optional): If set to :obj:`True` drop node types not connected by any edge type. (default: :obj:`False`) """ def __init__(self, metapaths: List[List[EdgeType]], drop_orig_edges: bool = False, keep_same_node_type: bool = False, drop_unconnected_nodes: bool = False): for path in metapaths: assert len(path) >= 2, f"Invalid metapath '{path}'" assert all([ j[-1] == path[i + 1][0] for i, j in enumerate(path[:-1]) ]), f"Invalid sequence of node types in '{path}'" self.metapaths = metapaths self.drop_orig_edges = drop_orig_edges self.keep_same_node_type = keep_same_node_type self.drop_unconnected_nodes = drop_unconnected_nodes def __call__(self, data: HeteroData) -> HeteroData: edge_types = data.edge_types # save original edge types data.metapath_dict = {} for j, metapath in enumerate(self.metapaths): for edge_type in metapath: assert data._to_canonical( edge_type) in edge_types, f"'{edge_type}' not present" edge_type = metapath[0] adj1 = SparseTensor.from_edge_index( edge_index=data[edge_type].edge_index, sparse_sizes=data[edge_type].size()) for i, edge_type in enumerate(metapath[1:]): adj2 = SparseTensor.from_edge_index( edge_index=data[edge_type].edge_index, sparse_sizes=data[edge_type].size()) adj1 = adj1 @ adj2 row, col, _ = adj1.coo() new_edge_type = (metapath[0][0], f'metapath_{j}', metapath[-1][-1]) data[new_edge_type].edge_index = torch.vstack([row, col]) data.metapath_dict[new_edge_type] = metapath if self.drop_orig_edges: for i in edge_types: if self.keep_same_node_type and i[0] == i[-1]: continue else: del data[i] # remove nodes not connected by any edge type. if self.drop_unconnected_nodes: new_edge_types = data.edge_types node_types = data.node_types connected_nodes = set() for i in new_edge_types: connected_nodes.add(i[0]) connected_nodes.add(i[-1]) for node in node_types: if node not in connected_nodes: del data[node] return data