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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.more » « less
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Shpilker, P. ; Freeman, J. ; McKelvie, H. ; Ashey, J. ; Fonticella, J.M. ; Putnam, H. ; Greenberg, J. ; Cowen, L. ; Couch, A. ; Daniels, N.M. ( , Metadata and Semantic Research. MTSR 2021.)Garoufallou, E. ; Ovalle-Perandones, MA. ; Vlachidis, A (Ed.)
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Sledzieski, S. ; Singh, R. ; Cowen, L. ; Berger, B. ( , Research in Computational Molecular Biology 25th Annual International Conference (RECOMB 2021))Ma, Jian (Ed.)