Sublinear update time randomized algorithms for dynamic graph regression

Mostafa Haghir Chehreghani

Applied Mathematics and Computation, 2021, vol. 410, issue C

Abstract: A well-known problem in data science and machine learning is linear regression, which is recently extended to dynamic graphs. Existing exact algorithms for updating the solution of dynamic graph regression require at least a linear time (in terms of n: the size of the graph). However, this time complexity might be intractable in practice.

Keywords: Dynamic networks; Dynamic graph regression; Least squares regression; Sublinear update time; Subsampled randomized Hadamard transform; CountSketch (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:410:y:2021:i:c:s0096300321005233

DOI: 10.1016/j.amc.2021.126434

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