Github user jkbradley commented on the issue:
https://github.com/apache/spark/pull/15770
Yep, that's correct. Everyone, please let me know if you disagree.
Also, if we do go with Option 2 above, then the input schema could be a few
possible things:
* list of (neighbor ID, weight)
* pro: ensures # neighbors = # weights
* con: nesting can be annoying to work with
* (list of neighbor IDs, list of weights)
* pro: Easier to support unweighted graphs by omitting list of weights.
Avoid nesting.
* con: could have invalid input with mismatched numbers of neighbors,
weights
* Vector (of length # neighbors)
* pro: Familiar data type. Ensures # neighbors = # weights.
* con: If we later support passing raw features which are used to compute
a similarity matrix within PIC, then this could confuse users.
I'm ambivalent about these schema options. What do you think?
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