dmlc--dgl
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ReStructuredText
47 行
2.0 KiB
ReStructuredText
.. _guide-message-passing-heterograph:
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2.5 Message Passing on Heterogeneous Graph
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------------------------------------------
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:ref:`(中文版) <guide_cn-message-passing-heterograph>`
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Heterogeneous graphs (:ref:`guide-graph-heterogeneous`), or
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heterographs for short, are graphs that contain different types of nodes
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and edges. The different types of nodes and edges tend to have different
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types of attributes that are designed to capture the characteristics of
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each node and edge type. Within the context of graph neural networks,
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depending on their complexity, certain node and edge types might need to
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be modeled with representations that have a different number of
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dimensions.
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The message passing on heterographs can be split into two parts:
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1. Message computation and aggregation for each relation r.
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2. Reduction that merges the aggregation results from all relations for each node type.
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DGL’s interface to call message passing on heterographs is
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:meth:`~dgl.DGLGraph.multi_update_all`.
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:meth:`~dgl.DGLGraph.multi_update_all` takes a dictionary containing
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the parameters for :meth:`~dgl.DGLGraph.update_all` within each relation
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using relation as the key, and a string representing the cross type reducer.
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The reducer can be one of ``sum``, ``min``, ``max``, ``mean``, ``stack``.
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Here’s an example:
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.. code::
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import dgl.function as fn
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for c_etype in G.canonical_etypes:
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srctype, etype, dsttype = c_etype
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Wh = self.weight[etype](feat_dict[srctype])
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# Save it in graph for message passing
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G.nodes[srctype].data['Wh_%s' % etype] = Wh
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# Specify per-relation message passing functions: (message_func, reduce_func).
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# Note that the results are saved to the same destination feature 'h', which
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# hints the type wise reducer for aggregation.
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funcs[etype] = (fn.copy_u('Wh_%s' % etype, 'm'), fn.mean('m', 'h'))
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# Trigger message passing of multiple types.
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G.multi_update_all(funcs, 'sum')
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# return the updated node feature dictionary
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return {ntype : G.nodes[ntype].data['h'] for ntype in G.ntypes}
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