torch_geometric.transforms.AddMetaPaths
- class AddMetaPaths(metapaths: List[List[Tuple[str, str, str]]], drop_orig_edge_types: bool = False, keep_same_node_type: bool = False, drop_unconnected_node_types: bool = False, max_sample: Optional[int] = None, weighted: bool = False, **kwargs)[source]
Bases:
BaseTransform
Adds additional edge types to a
HeteroData
object between the source node type and the destination node type of a givenmetapath
, as described in the “Heterogenous Graph Attention Networks” paper (functional name: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
\[\mathcal{V}_1 \xrightarrow{R_1} \mathcal{V}_2 \xrightarrow{R_2} \ldots \xrightarrow{R_{\ell-1}} \mathcal{V}_{\ell}\]in which \(\mathcal{V}_i\) represents node types, and \(R_j\) 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
HeteroData
object as edge type(src_node_type, "metapath_*", dst_node_type)
, wheresrc_node_type
anddst_node_type
denote \(\mathcal{V}_1\) and \(\mathcal{V}_{\ell}\), repectively.In addition, a
metapath_dict
object is added to theHeteroData
object which maps the metapath-based edge type to its original metapath.from torch_geometric.datasets import DBLP from torch_geometric.data 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")]}
- Parameters:
metapaths (List[List[Tuple[str, str, str]]]) – The metapaths described by a list of lists of
(src_node_type, rel_type, dst_node_type)
tuples.drop_orig_edge_types (bool, optional) – If set to
True
, existing edge types will be dropped. (default:False
)keep_same_node_type (bool, optional) – If set to
True
, existing edge types between the same node type are not dropped even in casedrop_orig_edge_types
is set toTrue
. (default:False
)drop_unconnected_node_types (bool, optional) – If set to
True
, will drop node types not connected by any edge type. (default:False
)max_sample (int, optional) – If set, will sample at maximum
max_sample
neighbors within metapaths. Useful in order to tackle very dense metapath edges. (default:None
)weighted (bool, optional) – If set to
True
compute weights for each metapath edge and store them inedge_weight
. The weight of each metapath edge is computed as the number of metapaths from the start to the end of the metapath edge. (defaultFalse
)