# 3.3 Heterogeneous GraphConv Module¶

(中文版)

HeteroGraphConv is a module-level encapsulation to run DGL NN module on heterogeneous graphs. The implementation logic is the same as message passing level API multi_update_all(), including:

• DGL NN module within each relation $$r$$.

• Reduction that merges the results on the same node type from multiple relations.

This can be formulated as:

$h_{dst}^{(l+1)} = \underset{r\in\mathcal{R}, r_{dst}=dst}{AGG} (f_r(g_r, h_{r_{src}}^l, h_{r_{dst}}^l))$

where $$f_r$$ is the NN module for each relation $$r$$, $$AGG$$ is the aggregation function.

## HeteroGraphConv implementation logic:¶

import torch.nn as nn

class HeteroGraphConv(nn.Module):
def __init__(self, mods, aggregate='sum'):
super(HeteroGraphConv, self).__init__()
self.mods = nn.ModuleDict(mods)
if isinstance(aggregate, str):
# An internal function to get common aggregation functions
self.agg_fn = get_aggregate_fn(aggregate)
else:
self.agg_fn = aggregate


The heterograph convolution takes a dictionary mods that maps each relation to an nn module and sets the function that aggregates results on the same node type from multiple relations.

def forward(self, g, inputs, mod_args=None, mod_kwargs=None):
if mod_args is None:
mod_args = {}
if mod_kwargs is None:
mod_kwargs = {}
outputs = {nty : [] for nty in g.dsttypes}


Besides input graph and input tensors, the forward() function takes two additional dictionary parameters mod_args and mod_kwargs. These two dictionaries have the same keys as self.mods. They are used as customized parameters when calling their corresponding NN modules in self.mods for different types of relations.

An output dictionary is created to hold output tensor for each destination type nty . Note that the value for each nty is a list, indicating a single node type may get multiple outputs if more than one relations have nty as the destination type. HeteroGraphConv will perform a further aggregation on the lists.

if g.is_block:
src_inputs = inputs
dst_inputs = {k: v[:g.number_of_dst_nodes(k)] for k, v in inputs.items()}
else:
src_inputs = dst_inputs = inputs

for stype, etype, dtype in g.canonical_etypes:
rel_graph = g[stype, etype, dtype]
if rel_graph.num_edges() == 0:
continue
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)


The input g can be a heterogeneous graph or a subgraph block from a heterogeneous graph. As in ordinary NN module, the forward() function need to handle different input graph types separately.

Each relation is represented as a canonical_etype, which is (stype, etype, dtype). Using canonical_etype as the key, one can extract out a bipartite graph rel_graph. For bipartite graph, the input feature will be organized as a tuple (src_inputs[stype], dst_inputs[dtype]). The NN module for each relation is called and the output is saved. To avoid unnecessary call, relations with no edges or no nodes with the src type will be skipped.

rsts = {}
for nty, alist in outputs.items():
if len(alist) != 0:
rsts[nty] = self.agg_fn(alist, nty)


Finally, the results on the same destination node type from multiple relations are aggregated using self.agg_fn function. Examples can be found in the API Doc for HeteroGraphConv.