dgl.to_networkx¶
-
dgl.
to_networkx
(g, node_attrs=None, edge_attrs=None)[source]¶ Convert a homogeneous graph to a NetworkX graph and return.
The resulting NetworkX graph also contains the node/edge features of the input graph. Additionally, DGL saves the edge IDs as the
'id'
edge attribute in the returned NetworkX graph.- Parameters
g (DGLGraph) – A homogeneous graph.
node_attrs (iterable of str, optional) – The node attributes to copy from
g.ndata
. (Default: None)edge_attrs (iterable of str, optional) – The edge attributes to copy from
g.edata
. (Default: None)
- Returns
The converted NetworkX graph.
- Return type
Notes
The function only supports CPU graph input.
Examples
The following example uses PyTorch backend.
>>> import dgl >>> import torch
>>> g = dgl.graph((torch.tensor([1, 2]), torch.tensor([1, 3]))) >>> g.ndata['h'] = torch.zeros(4, 1) >>> g.edata['h1'] = torch.ones(2, 1) >>> g.edata['h2'] = torch.zeros(2, 2) >>> nx_g = dgl.to_networkx(g, node_attrs=['h'], edge_attrs=['h1', 'h2']) >>> nx_g.nodes(data=True) NodeDataView({0: {'h': tensor([0.])}, 1: {'h': tensor([0.])}, 2: {'h': tensor([0.])}, 3: {'h': tensor([0.])}}) >>> nx_g.edges(data=True) OutMultiEdgeDataView([(1, 1, {'id': 0, 'h1': tensor([1.]), 'h2': tensor([0., 0.])}), (2, 3, {'id': 1, 'h1': tensor([1.]), 'h2': tensor([0., 0.])})])