Add support for edge weights to GraphSAGE sampling #1667
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Code Climate has analyzed commit 7b86b92 and detected 4 issues on this pull request. Here's the issue category breakdown:
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stellargraph/data/explorer.py
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| else: | ||
| neighbours = [] | ||
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| if len(neighbours) == 0: |
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I wonder if we should do something like:
if len([w for w in weights if w != 0]) == 0:so that we still have -1s propagated if all neighbours have zero weight?
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Hm, that's a good point. That particular form is likely to be expensive because it's manually iterating the array, but it's probably worth doing something with https://docs.scipy.org/doc/numpy/reference/generated/numpy.count_nonzero.html or using the final value of the cumsum in naive_weighted_choice. I'll probably do it in the latter, because then it applies to weighted Node2Vec too (which currently doesn't handle this case).
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Done, and fixed/tested in Node2Vec and CTDNE as appropriate as well.
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| @@ -1117,6 +1128,8 @@ def _step(self, node, time, bias_type, np_rs): | |||
| if len(neighbours) > 0: | |||
| biases = self._temporal_biases(times, time, bias_type, is_forward=True) | |||
| chosen_neighbour_index = self._sample(len(neighbours), biases, np_rs) | |||
| assert chosen_neighbour_index is not None, "biases should never be all zero" | |||
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Use of assert detected. The enclosed code will be removed when compiling to optimised byte code.
This expands GraphSAGE (undirected and directed) to support weighted sampling, where edges with higher weights are taken proportionally more often.
For example, suppose there's there's 4 edges from node A:
An unweighed walk starting at A will choose each of the edges with equal propability and so end up on B, C or D in proportion 1:1:2 (edge counts). A weighted walk will choose the edges proportional to the weights, so end up on the vertices in proportion 0:1:5 (sum of edge weight). (Worth specifically highlighting: a weighted walk will never chose the A-B edge because it has weight 0.)
See: #462