Randomly Generated Gaussian Scores
收藏arXiv2025-09-30 收录
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https://github.com/connorzhangxu/DistributedFastTopKSelection
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资源简介：
该数据集包含随机生成的符合高斯分布的分数，这些分数具有特定的方差，并经过量化处理以确保最小间隔。该数据集用于验证所提出的最top-k选择算法的有效性。此外，数据集涉及不同设置的模拟，包括改变图中节点和边的数量。规模涵盖从小型到大型网络（高达10000个节点）。任务是通过分布式平滑分位数估计来进行top-k选择。
This dataset contains randomly generated scores following a Gaussian distribution with a specified variance, which have been quantized to ensure a minimum spacing between adjacent values. It is utilized to validate the effectiveness of the proposed top-k selection algorithm. Furthermore, the dataset includes simulations under various settings, such as altering the number of nodes and edges in the graph, with scales ranging from small-scale to large-scale networks (up to 10,000 nodes). The core task of this dataset is to perform top-k selection via distributed smooth quantile estimation.
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