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

View ORCID ProfileRenming Liu, View ORCID ProfileArjun Krishnan
doi: https://doi.org/10.1101/2020.07.23.218487
Renming Liu
1Department of Computational Mathematics, Science and Engineering, Michigan State University, USA
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Arjun Krishnan
1Department of Computational Mathematics, Science and Engineering, Michigan State University, USA
2Department of Biochemistry and Molecular Biology, Michigan State University, USA
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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 and documentation are available at https://github.com/krishnanlab/pecanpy.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/krishnanlab/pecanpy

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 24, 2020.
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PecanPy: a fast, efficient, and parallelized Python implementation of node2vec
Renming Liu, Arjun Krishnan
bioRxiv 2020.07.23.218487; doi: https://doi.org/10.1101/2020.07.23.218487
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PecanPy: a fast, efficient, and parallelized Python implementation of node2vec
Renming Liu, Arjun Krishnan
bioRxiv 2020.07.23.218487; doi: https://doi.org/10.1101/2020.07.23.218487

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