.. _guide-message-passing-heterograph:

2.5 Message Passing on Heterogeneous Graph
------------------------------------------

Heterogeneous graphs (:ref:`guide-graph-heterogeneous`), or
heterographs for short, are graphs that contain different types of nodes
and edges. The different types of nodes and edges tend to have different
types of attributes that are designed to capture the characteristics of
each node and edge type. Within the context of graph neural networks,
depending on their complexity, certain node and edge types might need to
be modeled with representations that have a different number of
dimensions.

The message passing on heterographs can be split into two parts:

1. Message computation and aggregation for each relation r.
2. Reduction that merges the aggregation results from all relations for each node type.

DGL’s interface to call message passing on heterographs is
:meth:`~dgl.DGLGraph.multi_update_all`.
:meth:`~dgl.DGLGraph.multi_update_all` takes a dictionary containing
the parameters for :meth:`~dgl.DGLGraph.update_all` within each relation
using relation as the key, and a string representing the cross type reducer.
The reducer can be one of ``sum``, ``min``, ``max``, ``mean``, ``stack``.
Here’s an example:

.. code::

    import dgl.function as fn

    for c_etype in G.canonical_etypes:
        srctype, etype, dsttype = c_etype
        Wh = self.weight[etype](feat_dict[srctype])
        # Save it in graph for message passing
        G.nodes[srctype].data['Wh_%s' % etype] = Wh
        # Specify per-relation message passing functions: (message_func, reduce_func).
        # Note that the results are saved to the same destination feature 'h', which
        # hints the type wise reducer for aggregation.
        funcs[etype] = (fn.copy_u('Wh_%s' % etype, 'm'), fn.mean('m', 'h'))
    # Trigger message passing of multiple types.
    G.multi_update_all(funcs, 'sum')
    # return the updated node feature dictionary
    return {ntype : G.nodes[ntype].data['h'] for ntype in G.ntypes}
