PecanPy: a fast, efficient and parallelized Python implementation of node2vec.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 33760066.
- Also identified by DOI 10.1093/bioinformatics/btab202 and PMC identifier 8504639.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
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 precomputing/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.