Abstract
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.
The work of Cowen, Hu, and Wu was partially supported by the National Science Foundation under grant DMS-1812503, CCF-1934553, and OAC-2018149.
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Cowen, L.J., Hu, X., Lin, J., Shen, Y., Wu, K. (2022). Random-Walk Based Approximate k-Nearest Neighbors Algorithm for Diffusion State Distance. In: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2021. Lecture Notes in Computer Science, vol 13127. Springer, Cham. https://doi.org/10.1007/978-3-030-97549-4_1
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DOI: https://doi.org/10.1007/978-3-030-97549-4_1
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