%ACowen, L.%AHu, X.%ALin, J.%AShen, Y.%AWu, K.%BJournal Name: Large-Scale Scientific Computing. LSSC 2021, Springer Lecture Notes in Computer Science
%D2022%I
%JJournal Name: Large-Scale Scientific Computing. LSSC 2021, Springer Lecture Notes in Computer Science
%K
%MOSTI ID: 10346855
%PMedium: X
%TRandom-Walk Based Approximate k-Nearest Neighbors Algorithm for Diffusion State Distance
%XDiffusion 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 proteinsâ€™ functional labels.
%0Journal Article
Country unknown/Code not availablehttps://doi.org/10.1007/978-3-030-97549-4_1OSTI-MSA