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The example below builds an MP-GNN with three edge types, adds and multiplies edges between nodes of each edge type, and runs the MP-GNN on a toy dataset.The trained MP-GNN is then used to compute the shortest path between the nodes 2 and 5 and between the nodes 1 and 5.
# An input node connected to three other nodes # (nodes 1, 2, and 3). [ [1, 2, 3, 0], [2, 0, 3, 0], [3, 0, 0, 0] ]
where edges have 2 or 3 nodes (in-degree 2 and 3 respectively) and 0 as the label.
HeteroConv first applies the to_hetero() function to each edge (i.e. by edge type) to convert it to the
submodule for that type.This can then be followed by a number of node functions (e.g. max_pool, etc.).
# An input node connected to 6 other nodes (5 triangles and a singleton) # (nodes 1-5) [[[1, 2, 3, 0, 4, 5], [1, 4, 5, 0, 1, 2], [1, 4, 5, 0, 2, 3], [2, 3, 5, 0, 2, 4], [2, 3, 5, 0, 4, 5], [3, 4, 5, 0, 3, 4]], [5, 0, 1, 0, 5, 0], [5, 0, 2, 0, 5, 0], [5, 0, 3, 0, 5, 0], [5, 0, 4, 0, 0, 0]]]
For this graph, hetero_conv() would build a convolution wrapper like:
The idea is to define different operators depending on the edge type in the vocabulary (the key in the dictionary that contains a dictionary for each edge type).In this example, we define 2 operators to convert a node to a vector, and another one to convert it to a matrix.
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One of the drawbacks to this approach was that we needed to recompile every time we made even a small change to the code. This slowed everything else we were doing down and because we couldn't always have someone waiting for the application to finish building. 827ec27edc