Source code for dgl.merge

"""Utilities for merging graphs."""

import dgl
from . import backend as F
from .base import DGLError

__all__ = ['merge']

[docs]def merge(graphs): r"""Merge a sequence of graphs together into a single graph. Nodes and edges that exist in ``graphs[i+1]`` but not in ``dgl.merge(graphs[0:i+1])`` will be added to ``dgl.merge(graphs[0:i+1])`` along with their data. Nodes that exist in both ``dgl.merge(graphs[0:i+1])`` and ``graphs[i+1]`` will be updated with ``graphs[i+1]``'s data if they do not match. Parameters ---------- graphs : list[DGLGraph] Input graphs. Returns ------- DGLGraph The merged graph. Notes ---------- * Inplace updates are applied to a new, empty graph. * Features that exist in ``dgl.graphs[i+1]`` will be created in ``dgl.merge(dgl.graphs[i+1])`` if they do not already exist. Examples ---------- The following example uses PyTorch backend. >>> import dgl >>> import torch >>> g = dgl.graph((torch.tensor([0,1]), torch.tensor([2,3]))) >>> g.ndata["x"] = torch.zeros(4) >>> h = dgl.graph((torch.tensor([1,2]), torch.tensor([0,4]))) >>> h.ndata["x"] = torch.ones(5) >>> m = dgl.merge([g, h]) ``m`` now contains edges and nodes from ``h`` and ``g``. >>> m.edges() (tensor([0, 1, 1, 2]), tensor([2, 3, 0, 4])) >>> m.nodes() tensor([0, 1, 2, 3, 4]) ``g``'s data has updated with ``h``'s in ``m``. >>> m.ndata["x"] tensor([1., 1., 1., 1., 1.]) See Also ---------- add_nodes add_edges """ if len(graphs) == 0: raise DGLError('The input list of graphs cannot be empty.') ref = graphs[0] ntypes = ref.ntypes etypes = ref.canonical_etypes data_dict = {etype: ([], []) for etype in etypes} num_nodes_dict = {ntype: 0 for ntype in ntypes} merged = dgl.heterograph(data_dict, num_nodes_dict, ref.idtype, ref.device) # Merge edges and edge data. for etype in etypes: unmerged_us = [] unmerged_vs = [] edata_frames = [] for graph in graphs: etype_id = graph.get_etype_id(etype) us, vs = graph.edges(etype=etype) unmerged_us.append(us) unmerged_vs.append(vs) edge_data = graph._edge_frames[etype_id] edata_frames.append(edge_data) keys = ref.edges[etype].data.keys() if len(keys) == 0: edges_data = None else: edges_data = {k: F.cat([f[k] for f in edata_frames], dim=0) for k in keys} merged_us = F.copy_to(F.astype(F.cat(unmerged_us, dim=0), ref.idtype), ref.device) merged_vs = F.copy_to(F.astype(F.cat(unmerged_vs, dim=0), ref.idtype), ref.device) merged.add_edges(merged_us, merged_vs, edges_data, etype) # Add node data and isolated nodes from next_graph to merged. for next_graph in graphs: for ntype in ntypes: merged_ntype_id = merged.get_ntype_id(ntype) next_ntype_id = next_graph.get_ntype_id(ntype) next_ndata = next_graph._node_frames[next_ntype_id] node_diff = (next_graph.num_nodes(ntype=ntype) - merged.num_nodes(ntype=ntype)) n_extra_nodes = max(0, node_diff) merged.add_nodes(n_extra_nodes, ntype=ntype) next_nodes = F.arange( 0, next_graph.num_nodes(ntype=ntype), merged.idtype, merged.device ) merged._node_frames[merged_ntype_id].update_row( next_nodes, next_ndata ) return merged