"""Heterograph NN modules"""
from mxnet import nd
from mxnet.gluon import nn
__all__ = ['HeteroGraphConv']
[docs]class HeteroGraphConv(nn.Block):
r"""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:
.. 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 mxnet.ndarray as nd
>>> h1 = {'user' : nd.random.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:
.. code::
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 = mx.nd.stack(*tensors, axis=0)
return mx.nd.sum(stacked, axis=0)
Attributes
----------
mods : dict[str, nn.Module]
Modules associated with every edge types.
"""
def __init__(self, mods, aggregate='sum'):
super(HeteroGraphConv, self).__init__()
with self.name_scope():
for name, mod in mods.items():
self.register_child(mod, name)
self.mods = mods
# Do not break if graph has 0-in-degree nodes.
# Because there is no general rule to add self-loop for heterograph.
for _, v in self.mods.items():
set_allow_zero_in_degree_fn = getattr(v, 'set_allow_zero_in_degree', None)
if callable(set_allow_zero_in_degree_fn):
set_allow_zero_in_degree_fn(True)
if isinstance(aggregate, str):
self.agg_fn = get_aggregate_fn(aggregate)
else:
self.agg_fn = aggregate
[docs] def forward(self, 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
-------
dict[str, Tensor]
Output representations for every types of nodes.
"""
if mod_args is None:
mod_args = {}
if mod_kwargs is None:
mod_kwargs = {}
outputs = {nty : [] for nty in g.dsttypes}
if isinstance(inputs, tuple):
src_inputs, dst_inputs = inputs
for stype, etype, dtype in g.canonical_etypes:
rel_graph = g[stype, etype, dtype]
if stype not in src_inputs or dtype not in dst_inputs:
continue
dstdata = self.mods[etype](
rel_graph,
(src_inputs[stype], dst_inputs[dtype]),
*mod_args.get(etype, ()),
**mod_kwargs.get(etype, {}))
outputs[dtype].append(dstdata)
else:
for stype, etype, dtype in g.canonical_etypes:
rel_graph = g[stype, etype, dtype]
if stype not in inputs:
continue
dstdata = self.mods[etype](
rel_graph,
(inputs[stype], inputs[dtype]),
*mod_args.get(etype, ()),
**mod_kwargs.get(etype, {}))
outputs[dtype].append(dstdata)
rsts = {}
for nty, alist in outputs.items():
if len(alist) != 0:
rsts[nty] = self.agg_fn(alist, nty)
return rsts
def __repr__(self):
summary = 'HeteroGraphConv({\n'
for name, mod in self.mods.items():
summary += ' {} : {},\n'.format(name, mod)
summary += '\n})'
return summary
def get_aggregate_fn(agg):
"""Internal function to get the aggregation function for node data
generated from different relations.
Parameters
----------
agg : str
Method for aggregating node features generated by different relations.
Allowed values are 'sum', 'max', 'min', 'mean', 'stack'.
Returns
-------
callable
Aggregator function that takes a list of tensors to aggregate
and returns one aggregated tensor.
"""
if agg == 'sum':
fn = nd.sum
elif agg == 'max':
fn = nd.max
elif agg == 'min':
fn = nd.min
elif agg == 'mean':
fn = nd.mean
elif agg == 'stack':
fn = None # will not be called
else:
raise DGLError('Invalid cross type aggregator. Must be one of '
'"sum", "max", "min", "mean" or "stack". But got "%s"' % agg)
if agg == 'stack':
def stack_agg(inputs, dsttype): # pylint: disable=unused-argument
if len(inputs) == 0:
return None
return nd.stack(*inputs, axis=1)
return stack_agg
else:
def aggfn(inputs, dsttype): # pylint: disable=unused-argument
if len(inputs) == 0:
return None
stacked = nd.stack(*inputs, axis=0)
return fn(stacked, axis=0)
return aggfn