feat: implementation for group_degree_centrality, group_closeness_centrality, and group_betweenness_centrality

[selmanozleyen]

hi I added these three functions because I'd like to use them in squidpy instead of pure python version.

• Add group_degree_centrality, group_closeness_centrality, and
  group_betweenness_centrality for both digraph and graph

Note

While writing cross-validation tests against NetworkX, I discovered that
NetworkX's group_betweenness_centrality is buggy on directed graphs that
are not strongly connected (returns negative values, crashes with KeyError).
Filed upstream: networkx/networkx#8559.
The NX comparison tests here only use strongly-connected directed graphs to
avoid this.

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[IvanIsCoding]

Some preliminary thoughts:

1. Fix clippy lints, naturally
2. I'd decouple the tests from NetworkX. If you can just put the expected centrality in a dictionary that is fine. Naturally, document it with #obtained with: g_nx = nx.cycle_graph(6, create_using=nx.DiGraph) etc. so we can reproduce the numbers

Using NetworkX as a ground source of truth is a great idea. But it's also nice not to be affected by their API changes. We had to do that for other centrality methods.

[selmanozleyen]

thanks for the feedback! I made the lints + hardcoded the tests while documenting the nx version to get them.

I undid the unrelated formatting/cleanup to this PR here: 7904d20

I can revert it if you want to include them

[IvanIsCoding]

We changed the Python formatter so I guess the failures are on me. Oh well

just check https://github.com/Qiskit/rustworkx/blob/main/CONTRIBUTING.md, make nox -elint pass locally, add a release note. And then I can intervene and wrap it up before merging.

0.18 has been delayed regardless, so your PR will still make the release.

[selmanozleyen]

ok thanks I added the release note plus the lints