Modularitypruning

Add Python 3.11 testing and support

**Full Changelog**: https://github.com/ragibson/ModularityPruning/compare/v1.3.1...v1.3.2

Improve performance of multi-layer Leiden iteration and optimization.

For small numbers of layers, this is significantly faster than using the deprecated `modularitypruning.louvain_utilities`.

**Full Changelog**: https://github.com/ragibson/ModularityPruning/compare/v1.3.0...v1.3.1

Add Leiden modularity optimization

1. Replace Louvain modularity optimization (from the deprecated `louvain-igraph`) with Leiden (from `leidenalg`).
2. Update documentation and resolve deprecated uses of `scipy`, `matplotlib`, and `random.sample`.
3. Drop support for Python 3.7, matching much of the Python scientific community.

Overall, this should make the single-layer functions faster and multi-layer functions faster when the number of layers is small. As the number of layers grows, the inefficiencies of the `leidenalg` implementation will eventually make the algorithm extremely slow. As such, we keep the legacy Louvain multi-layer functionality in `modularitypruning.louvain_utilities`.

By dropping support for Python 3.7, the dependency lists are greatly simplified (many of our dependencies, including numpy and scipy, stopped supporting 3.7 last year).

**Full Changelog**: https://github.com/ragibson/ModularityPruning/compare/v1.2.3...v1.3.0

Update preprint link to published journal article, resolve various linting errors, and improve dependency checks for louvain/leidenalg.

Add clarification on the implication of bounds choices in the main pruning functions.

Add experiment showing modularity is not K-monotonic (the number of communities may actually temporarily decrease as the resolution parameter, gamma, increases).

Also increase the granularity of the scikit-learn dependency version in order to maintain support for Python 3.7. **Many** packages have dropped anything below 3.8, but I'm trying to not drop it so far ahead of its end-of-life date (near the end of June 2023) if possible.