Add a few more BFS-based algorithms

[eriknw]

BFS is a workhorse! There are still many BFS-based algorithms still to do.

My main TODO is to benchmark these to give them a proper shakedown. I like to shake down most algorithms we add. Also, I wonder whether we should use A.T or AT in a couple places. I also wonder if we can come up with a heuristic to detect negative cycles.

It may be nice to know whether directed graphs are structurally symmetric. If we knew this, we could improve is_weakly_connected and other algorithms.

Also, there may be a couple NetworkX bugs to fix or behaviors to match.

Implemented:

• Components
  • is_connected
  • is_weakly_connected
  • node_connected_component
• Shortest Paths
  • all_pairs_shortest_path_length
  • negative_edge_cycle
  • single_source_shortest_path_length
  • single_target_shortest_path_length
• Traversal
  • bfs_layers
  • descendants_at_distance

[eriknw]

I'm also not sure if these are the best algorithms for connected component-related functions. Connected components are an important class of algorithms that we should add soon (for example, we have an implementation of FastSV in a Jupyter notebook that we can port over), so perhaps we'll revisit these algorithms then.

[codecov-commenter]

Codecov Report

Patch coverage: 87.71% and project coverage change: +2.46 :tada:

Comparison is base (6de1fd6) 69.16% compared to head (029a639) 71.62%.

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Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #51 +/- ## ========================================== + Coverage 69.16% 71.62% +2.46% ========================================== Files 77 89 +12 Lines 3152 3461 +309 Branches 602 642 +40 ========================================== + Hits 2180 2479 +299 + Misses 786 783 -3 - Partials 186 199 +13 ``` | [Impacted Files](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas) | Coverage Δ | | |---|---|---| | [graphblas\_algorithms/nxapi/\_utils.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvbnhhcGkvX3V0aWxzLnB5) | `57.47% <0.00%> (ø)` | | | [graphblas\_algorithms/nxapi/cluster.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvbnhhcGkvY2x1c3Rlci5weQ==) | `85.55% <ø> (+7.55%)` | :arrow_up: | | [graphblas\_algorithms/classes/nodeset.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvY2xhc3Nlcy9ub2Rlc2V0LnB5) | `69.44% <42.85%> (-2.20%)` | :arrow_down: | | [...as\_algorithms/algorithms/link\_analysis/hits\_alg.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvYWxnb3JpdGhtcy9saW5rX2FuYWx5c2lzL2hpdHNfYWxnLnB5) | `60.86% <50.00%> (ø)` | | | [...blas\_algorithms/nxapi/shortest\_paths/unweighted.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvbnhhcGkvc2hvcnRlc3RfcGF0aHMvdW53ZWlnaHRlZC5weQ==) | `62.50% <62.50%> (ø)` | | | [graphblas\_algorithms/classes/nodemap.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvY2xhc3Nlcy9ub2RlbWFwLnB5) | `50.67% <75.00%> (+1.70%)` | :arrow_up: | | [...s\_algorithms/algorithms/shortest\_paths/weighted.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvYWxnb3JpdGhtcy9zaG9ydGVzdF9wYXRocy93ZWlnaHRlZC5weQ==) | `31.36% <78.12%> (+11.21%)` | :arrow_up: | | [...algorithms/algorithms/shortest\_paths/unweighted.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvYWxnb3JpdGhtcy9zaG9ydGVzdF9wYXRocy91bndlaWdodGVkLnB5) | `78.57% <78.57%> (ø)` | | | [graphblas\_algorithms/algorithms/dag.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvYWxnb3JpdGhtcy9kYWcucHk=) | `94.28% <81.81%> (+0.73%)` | :arrow_up: | | [...as\_algorithms/algorithms/shortest\_paths/generic.py](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas#diff-Z3JhcGhibGFzX2FsZ29yaXRobXMvYWxnb3JpdGhtcy9zaG9ydGVzdF9wYXRocy9nZW5lcmljLnB5) | `86.66% <92.30%> (+1.48%)` | :arrow_up: | | ... and [26 more](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas) | | ... and [1 file with indirect coverage changes](https://codecov.io/gh/python-graphblas/graphblas-algorithms/pull/51/indirect-changes?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas) Help us with your feedback. Take ten seconds to tell us [how you rate us](https://about.codecov.io/nps?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas). Have a feature suggestion? [Share it here.](https://app.codecov.io/gh/feedback/?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=python-graphblas)

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[eriknw]

Merging so we can do a release before PyConUS. May be able to benchmark and shakedown some algorithms later, but they're probably good enough.