PecanPy: a fast, efficient, and parallelized Python implementation of node2vec

Renming Liu, Arjun Krishnan
2021 Bioinformatics  
Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks. Node2vec is the most widely used method for node embedding. However, its original Python and C ++ implementations scale poorly with network density, failing for dense biological networks with hundreds of millions of edges. We have developed PecanPy, a new Python implementation of node2vec that uses cache-optimized compact graph data structures and
more » ... computing/parallelization to result in fast, high-quality node embeddings for biological networks of all sizes and densities. PecanPy software is freely available at https://github.com/krishnanlab/PecanPy. Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btab202 pmid:33760066 pmcid:PMC8504639 fatcat:ed6psnd4b5hq5fekiaqfs6aapm