PecanPy：node2vec的一种快速、高效且并行化的Python实现。

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

作者信息

Liu Renming, Krishnan Arjun

机构信息

Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.

Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.

出版信息

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

DOI:10.1093/bioinformatics/btab202

PMID:33760066

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8504639/

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.

摘要

学习大型图中节点的低维表示（嵌入）是在大规模生物网络上应用机器学习的关键。Node2vec是最广泛使用的节点嵌入方法。然而，其原始的Python和C++实现随着网络密度的增加扩展性较差，对于具有数亿条边的密集生物网络会失效。我们开发了PecanPy，这是一种新的Node2vec的Python实现，它使用缓存优化的紧凑图数据结构以及预计算/并行化，可为各种规模和密度的生物网络生成快速、高质量的节点嵌入。