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Creators/Authors contains: "Cowen, L."

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  1. Diffusion State Distance (DSD) is a data-dependent metric that compares data points using a data-driven diffusion process and provides a powerful tool for learning the underlying structure of high-dimensional data. While finding the exact nearest neighbors in the DSD metric is computationally expensive, in this paper, we propose a new random-walk based algorithm that empirically finds approximate k-nearest neighbors accurately in an efficient manner. Numerical results for real-world protein-protein interaction networks are presented to illustrate the efficiency and robustness of the proposed algorithm. The set of approximate k-nearest neighbors performs well when used to predict proteins’ functional labels.
    Free, publicly-accessible full text available March 18, 2023
  2. Garoufallou, E. ; Ovalle-Perandones, MA. ; Vlachidis, A (Ed.)
    Free, publicly-accessible full text available April 1, 2023
  3. Ma, Jian (Ed.)