Accurately modeling biased random walks on weighted networks using node2vec.

Journal: Bioinformatics (Oxford, England)
PMID: 36688699

Abstract

MOTIVATION: Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction networks, are dense, weighted graphs, node2vec is fundamentally limited in its ability to use edge weights during the biased random walk generation process, thus under-using all the information in the network.

Authors

  • Renming Liu
    Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, MI 48824, USA.
  • Matthew Hirn
    Michigan State University, Department of Computational Mathematics, Science & Engineering, East Lansing, Michigan, United States.
  • Arjun Krishnan
    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA; Departments of Computational Mathematics, Science, and Engineering and Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA.

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