# BatchedDGLGraph – Enable batched graph operations¶

class dgl.BatchedDGLGraph(graph_list, node_attrs, edge_attrs)[source]

Class for batched DGL graphs.

A BatchedDGLGraph basically merges a list of small graphs into a giant graph so that one can perform message passing and readout over a batch of graphs simultaneously.

The nodes and edges are re-indexed with a new id in the batched graph with the rule below:

item Graph 1 Graph 2 Graph k
raw id 0, …, N1 0, …, N2 …, Nk
new id 0, …, N1 N1 + 1, …, N1 + N2 + 1 …, N1 + … + Nk + k - 1

The batched graph is read-only, i.e. one cannot further add nodes and edges. A RuntimeError will be raised if one attempts.

To modify the features in BatchedDGLGraph has no effect on the original graphs. See the examples below about how to work around.

Parameters: graph_list (iterable) – A collection of DGLGraph objects to be batched. node_attrs (None, str or iterable, optional) – The node attributes to be batched. If None, the BatchedDGLGraph object will not have any node attributes. By default, all node attributes will be batched. An error will be raised if graphs having nodes have different attributes. If str or iterable, this should specify exactly what node attributes to be batched. edge_attrs (None, str or iterable, optional) – Same as for the case of node_attrs

Examples

Create two DGLGraph objects.

Instantiation:

>>> import dgl
>>> import torch as th
>>> g1 = dgl.DGLGraph()
>>> g1.ndata['hv'] = th.tensor([[0.], [1.]])       # Initialize node features
>>> g1.edata['he'] = th.tensor([[0.]])             # Initialize edge features

>>> g2 = dgl.DGLGraph()
>>> g2.add_edges([0, 2], [1, 1])                   # Add edges 0 -> 1, 2 -> 1
>>> g2.ndata['hv'] = th.tensor([[2.], [3.], [4.]]) # Initialize node features
>>> g2.edata['he'] = th.tensor([[1.], [2.]])       # Initialize edge features


Merge two DGLGraph objects into one BatchedDGLGraph object. When merging a list of graphs, we can choose to include only a subset of the attributes.

>>> bg = dgl.batch([g1, g2], edge_attrs=None)
>>> bg.edata
{}


Below one can see that the nodes are re-indexed. The edges are re-indexed in the same way.

>>> bg.nodes()
tensor([0, 1, 2, 3, 4])
>>> bg.ndata['hv']
tensor([[0.],
[1.],
[2.],
[3.],
[4.]])


Property:

We can still get a brief summary of the graphs that constitute the batched graph.

>>> bg.batch_size
2
>>> bg.batch_num_nodes
[2, 3]
>>> bg.batch_num_edges
[1, 2]


Another common demand for graph neural networks is graph readout, which is a function that takes in the node attributes and/or edge attributes for a graph and outputs a vector summarizing the information in the graph. BatchedDGLGraph also supports performing readout for a batch of graphs at once.

Below we take the built-in readout function sum_nodes() as an example, which sums over a particular kind of node attribute for each graph.

>>> dgl.sum_nodes(bg, 'hv') # Sum the node attribute 'hv' for each graph.
tensor([[1.],               # 0 + 1
[9.]])              # 2 + 3 + 4


Message passing:

For message passing and related operations, BatchedDGLGraph acts exactly the same as DGLGraph.

Update Attributes:

Updating the attributes of the batched graph has no effect on the original graphs.

>>> bg.edata['he'] = th.zeros(3, 2)
>>> g2.edata['he']
tensor([[1.],
[2.]])}


Instead, we can decompose the batched graph back into a list of graphs and use them to replace the original graphs.

>>> g1, g2 = dgl.unbatch(bg)    # returns a list of DGLGraph objects
>>> g2.edata['he']
tensor([[0., 0.],
[0., 0.]])}


## Merge and decompose¶

 batch(graph_list[, node_attrs, edge_attrs]) Batch a collection of DGLGraph and return a BatchedDGLGraph object that is independent of the graph_list. unbatch(graph) Return the list of graphs in this batch.

## Query batch summary¶

 BatchedDGLGraph.batch_size Number of graphs in this batch. BatchedDGLGraph.batch_num_nodes Number of nodes of each graph in this batch. BatchedDGLGraph.batch_num_edges Number of edges of each graph in this batch.

 sum_nodes(graph, feat[, weight]) Sums all the values of node field feat in graph, optionally multiplies the field by a scalar node field weight. sum_edges(graph, feat[, weight]) Sums all the values of edge field feat in graph, optionally multiplies the field by a scalar edge field weight. mean_nodes(graph, feat[, weight]) Averages all the values of node field feat in graph, optionally multiplies the field by a scalar node field weight. mean_edges(graph, feat[, weight]) Averages all the values of edge field feat in graph, optionally multiplies the field by a scalar edge field weight. max_nodes(graph, feat) Take elementwise maximum over all the values of node field feat in graph max_edges(graph, feat) Take elementwise maximum over all the values of edge field feat in graph topk_nodes(graph, feat, k[, descending, idx]) Return graph-wise top-k node features of field feat in graph ranked by keys at given index idx. topk_edges(graph, feat, k[, descending, idx]) Return graph-wise top-k edge features of field feat in graph ranked by keys at given index idx. softmax_nodes(graph, feat) Apply batch-wise graph-level softmax over all the values of node field feat in graph. softmax_edges(graph, feat) Apply batch-wise graph-level softmax over all the values of edge field feat in graph. broadcast_nodes(graph, feat_data) Broadcast feat_data to all nodes in graph, and return a tensor of node features. broadcast_edges(graph, feat_data) Broadcast feat_data to all edges in graph, and return a tensor of edge features.