Function in_subgraph

python/dgl/subgraph.py:342–474

Return the subgraph induced on the inbound edges of all the edge types of the given nodes. An in subgraph is equivalent to creating a new graph using the incoming edges of the given nodes. In addition to extracting the subgraph, DGL also copies the features of the extracted nodes an

(
    graph, nodes, *, relabel_nodes=False, store_ids=True, output_device=None
)

def in_subgraph(
    graph, nodes, *, relabel_nodes=False, store_ids=True, output_device=None
):
    """Return the subgraph induced on the inbound edges of all the edge types of the
    given nodes.

    An in subgraph is equivalent to creating a new graph using the incoming edges of the
    given nodes. In addition to extracting the subgraph, DGL also copies the features of
    the extracted nodes and edges to the resulting graph. The copy is *lazy* and incurs
    data movement only when needed.

    If the graph is heterogeneous, DGL extracts a subgraph per relation and composes
    them as the resulting graph. Thus, the resulting graph has the same set of relations
    as the input one.

    Parameters
    ----------
    graph : DGLGraph
        The input graph.
    nodes : nodes or dict[str, nodes]
        The nodes to form the subgraph, which cannot have any duplicate value. The result
        will be undefined otherwise. The allowed nodes formats are:

        * Int Tensor: Each element is a node ID. The tensor must have the same device type
          and ID data type as the graph's.
        * iterable[int]: Each element is a node ID.

        If the graph is homogeneous, one can directly pass the above formats.
        Otherwise, the argument must be a dictionary with keys being node types
        and values being the node IDs in the above formats.
    relabel_nodes : bool, optional
        If True, it will remove the isolated nodes and relabel the rest nodes in the
        extracted subgraph.
    store_ids : bool, optional
        If True, it will store the raw IDs of the extracted edges in the ``edata`` of the
        resulting graph under name ``dgl.EID``; if ``relabel_nodes`` is ``True``, it will
        also store the raw IDs of the extracted nodes in the ``ndata`` of the resulting
        graph under name ``dgl.NID``.
    output_device : Framework-specific device context object, optional
        The output device.  Default is the same as the input graph.

    Returns
    -------
    DGLGraph
        The subgraph.

    Notes
    -----

    This function discards the batch information. Please use
    :func:`dgl.DGLGraph.set_batch_num_nodes`
    and :func:`dgl.DGLGraph.set_batch_num_edges` on the transformed graph
    to maintain the information.

    Examples
    --------
    The following example uses PyTorch backend.