HeteroGraphConv

class dgl.nn.pytorch.HeteroGraphConv(mods, aggregate='sum')

Bases: torch.nn.modules.module.Module

A generic module for computing convolution on heterogeneous graphs.

The heterograph convolution applies sub-modules on their associating relation graphs, which reads the features from source nodes and writes the updated ones to destination nodes. If multiple relations have the same destination node types, their results are aggregated by the specified method. If the relation graph has no edge, the corresponding module will not be called.

Pseudo-code:

outputs = {nty : [] for nty in g.dsttypes}
# Apply sub-modules on their associating relation graphs in parallel
for relation in g.canonical_etypes:
    stype, etype, dtype = relation
    dstdata = relation_submodule(g[relation], ...)
    outputs[dtype].append(dstdata)

# Aggregate the results for each destination node type
rsts = {}
for ntype, ntype_outputs in outputs.items():
    if len(ntype_outputs) != 0:
        rsts[ntype] = aggregate(ntype_outputs)
return rsts

Examples

Create a heterograph with three types of relations and nodes.

>>> import dgl
>>> g = dgl.heterograph({
...     ('user', 'follows', 'user') : edges1,
...     ('user', 'plays', 'game') : edges2,
...     ('store', 'sells', 'game')  : edges3})

Create a HeteroGraphConv that applies different convolution modules to different relations. Note that the modules for 'follows' and 'plays' do not share weights.

>>> import dgl.nn.pytorch as dglnn
>>> conv = dglnn.HeteroGraphConv({
...     'follows' : dglnn.GraphConv(...),
...     'plays' : dglnn.GraphConv(...),
...     'sells' : dglnn.SAGEConv(...)},
...     aggregate='sum')

Call forward with some 'user' features. This computes new features for both 'user' and 'game' nodes.

>>> import torch as th
>>> h1 = {'user' : th.randn((g.number_of_nodes('user'), 5))}
>>> h2 = conv(g, h1)
>>> print(h2.keys())
dict_keys(['user', 'game'])

Call forward with both 'user' and 'store' features. Because both the 'plays' and 'sells' relations will update the 'game' features, their results are aggregated by the specified method (i.e., summation here).

>>> f1 = {'user' : ..., 'store' : ...}
>>> f2 = conv(g, f1)
>>> print(f2.keys())
dict_keys(['user', 'game'])

Call forward with some 'store' features. This only computes new features for 'game' nodes.

>>> g1 = {'store' : ...}
>>> g2 = conv(g, g1)
>>> print(g2.keys())
dict_keys(['game'])

Call forward with a pair of inputs is allowed and each submodule will also be invoked with a pair of inputs.

>>> x_src = {'user' : ..., 'store' : ...}
>>> x_dst = {'user' : ..., 'game' : ...}
>>> y_dst = conv(g, (x_src, x_dst))
>>> print(y_dst.keys())
dict_keys(['user', 'game'])

Parameters

• mods (dict[str, nn.Module]) – Modules associated with every edge types. The forward function of each module must have a DGLHeteroGraph object as the first argument, and its second argument is either a tensor object representing the node features or a pair of tensor object representing the source and destination node features.

• aggregate (str, callable, optional) –

  Method for aggregating node features generated by different relations. Allowed string values are ‘sum’, ‘max’, ‘min’, ‘mean’, ‘stack’. The ‘stack’ aggregation is performed along the second dimension, whose order is deterministic. User can also customize the aggregator by providing a callable instance. For example, aggregation by summation is equivalent to the follows:

  def my_agg_func(tensors, dsttype):
      # tensors: is a list of tensors to aggregate
      # dsttype: string name of the destination node type for which the
      #          aggregation is performed
      stacked = torch.stack(tensors, dim=0)
      return torch.sum(stacked, dim=0)
  

mods

Modules associated with every edge types.

Type

dict[str, nn.Module]

forward(g, inputs, mod_args=None, mod_kwargs=None)

Forward computation

Invoke the forward function with each module and aggregate their results.

Parameters

• g (DGLHeteroGraph) – Graph data.

• inputs (dict[str, Tensor] or pair of dict[str, Tensor]) – Input node features.

• mod_args (dict[str, tuple[any]], optional) – Extra positional arguments for the sub-modules.

• mod_kwargs (dict[str, dict[str, any]], optional) – Extra key-word arguments for the sub-modules.

Returns

Output representations for every types of nodes.

Return type

dict[str, Tensor]