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

Renming Liu; Arjun Krishnan

doi:10.1093/bioinformatics/btab202

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

Bioinformatics. 2021 Oct 11;37(19):3377-3379. doi: 10.1093/bioinformatics/btab202.

Authors

Renming Liu¹, Arjun Krishnan^{1

2}

Affiliations

¹ Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
² Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.

Abstract

Summary: 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.

Availabilityand implementation: PecanPy software is freely available at https://github.com/krishnanlab/PecanPy.

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

Grants and funding