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.