nodevectors implements many more algorithms than fastnode2vec that focuses on node2vec.
I'm pretty sure fastnode2vec is the fastest node2vec implementation because it uses CSR format, JIT compilation but also supports multiprocessing. The sampling algorithm has been improved compared to the paper (and to all other implementations) and allows truly linear memory consumption.
On the other hand, nodevectors has a lot of very cool methods like ProNE so you should definitely try it on your data. However, the original ProNE code is probably faster as it is written in C++ and uses multiple cores while nodevectors just uses the installed BLAS (you can probably get a massive speedup by installing OpenBLAS).
nodevectors implements many more algorithms than fastnode2vec that focuses on node2vec. I'm pretty sure fastnode2vec is the fastest node2vec implementation because it uses CSR format, JIT compilation but also supports multiprocessing. The sampling algorithm has been improved compared to the paper (and to all other implementations) and allows truly linear memory consumption.
On the other hand, nodevectors has a lot of very cool methods like ProNE so you should definitely try it on your data. However, the original ProNE code is probably faster as it is written in C++ and uses multiple cores while nodevectors just uses the installed BLAS (you can probably get a massive speedup by installing OpenBLAS).
[1]: https://github.com/louisabraham/fastnode2vec