Reproducibility Report for ACM SIGMOD 2024 Paper: 'Faster Algorithms for Fair Max-Min Diversification in <i> R <sup>d</sup> </i> '

This report evaluates the reproducibility of the paper titled ''Faster Algorithms for Fair Max-Min Diversification in Rd''. The paper's artifacts were available and evaluatable. Reproducibility was straightforward. The paper's core claims were reproduced, with minor variations in a few graphs.

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Publication Details

Published
2025-03-21
DOI
https://doi.org/10.1145/3687998.3717058
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Reproducibility Report for ACM SIGMOD 2024 Paper: 'Faster Algorithms for Fair Max-Min Diversification in R d '

William Zhang, Lukas Schwerdtfeger
Data Mining Algorithms and Applications
article

Reproducibility Report for ACM SIGMOD 2024 Paper: 'Faster Algorithms for Fair Max-Min Diversification in R d '

William Zhang, Lukas Schwerdtfeger
article en

Abstract

This report evaluates the reproducibility of the paper titled ''Faster Algorithms for Fair Max-Min Diversification in Rd''. The paper's artifacts were available and evaluatable. Reproducibility was straightforward. The paper's core claims were reproduced, with minor variations in a few graphs.

Technische Universität Berlin (DE), Carnegie Mellon University (US)
Openalex Percentile: Top 93%
Data Mining Algorithms and Applications
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Reproducibility Report for ACM SIGMOD 2024 Paper: 'Faster Algorithms for Fair Max-Min Diversification in <i> R <sup>d</sup> </i> ' — William Zhang, Lukas Schwerdtfeger · (2025) | TGRS Research Map | TGRS