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

Author:

Liu Renming1,Krishnan Arjun12ORCID

Affiliation:

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

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.

Funder

US National Institutes of Health

MSU

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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