Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 97.19%. Comparing base (1d94b7a) to head (e1cab70).
⚠️ Report is 1 commits behind head on master.

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  Coverage   97.19%   97.19%           
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  Files         121      121           
  Lines        7092     7105   +13     
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+ Hits         6893     6906   +13     
  Misses        199      199           

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Rough idea for alternative implementation approach: uniquely represent the graph with matrices (a SimpleGraph with an adjacency matrix, a SimpleDiGraph with a weights matrix and an adjacency matrix). Then it's possible to rely on the hash(::AbstractArray, ::UInt) implementation that comes with Julia.

I don't think converting to matrices is a good idea, hash may be called a lot for some operations and building adjacency matrices allocates a ton

I removed the xor in the last version. After checking other projects, the UInt is the hardcoded assumption everywhere so we should probably keep it

I don't think converting to matrices is a good idea, hash may be called a lot for some operations and building adjacency matrices allocates a ton

The idea is to have an AbstractMatrix that's not a dense representation. However this bugfix PR should probably be merged ASAP in any case.

even allocating a sparse matrix is not a no-op, it does allocate, I don't think it should be the case for hash when it can be avoided

I modified the PR title to be a bit more descriptive because currently we do not have a changelog file and the PR title is the only thing that will show up in the autogenerated github release notes.

I also bumped the version so that we can make an immediate release?

@nsajko , do you consent to my suggestion to keep the code simple at the expense of a non-allocating typestable slowdown in <1.12? I completely agree with the factual technical statements you are making, I just want to have an evaluation how strongly you feel about the more-subjective judgement call of whether this is a bad policy.

I consent. I don't feel strongly about Julia LTS performance.

great, thanks for the feedback

I'll let someone else merge it to avoid merging my own PR