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Fast distributed k-nn graph update

Abstract : In this paper, we present an approximate algorithm that is able to quickly modify a large distributed k-nn graph by adding or removing nodes. The algorithm produces an approximate graph that is highly similar to the graph computed using a naïve approach, although it requires the computation of far fewer similarities. To achieve this goal, it relies on a novel, distributed graph based search procedure. All these algorithms are also experimentally evaluated, using both euclidean and non-euclidean datasets.
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Submitted on : Monday, May 29, 2017 - 2:56:06 PM
Last modification on : Friday, June 2, 2017 - 11:02:47 AM
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Thibault Debatty, Fabio Pulvirenti, Pietro Michiardi, Wim Mees. Fast distributed k-nn graph update. 2016 IEEE International Conference on Big Data, Dec 2016, Washington, DC, United States. pp.3308-3317, ⟨10.1109/BigData.2016.7840990⟩. ⟨hal-01525697⟩



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