Large-scale temporal graph datasets

NIAID Data Ecosystem2026-03-13 收录

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https://zenodo.org/record/5937375

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资源简介：

Large scale datasets used for evaluation in the article: `Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms` to appear in `EuroSys 2022`.

创建时间：

2022-02-02

Large-scale temporal graph datasets

Large scale datasets used for evaluation in the article: `Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms` to appear in `EuroSys 2022`.

Zenodo2022-02-01 更新00

Large-scale temporal graph datasets

Large scale datasets used for evaluation in the article: `Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms` to appear in `EuroSys 2022`.

Zenodo2022-02-01 更新00

Large-Scale Dynamic Random Graph - Example

Zhang et al. (https://link.springer.com/article/10.1140/epjb/e2017-80122-8) suggest a temporal random network with changing dynamics that follow a Markov process, allowing for a continuous-time networ

Figshare2022-08-10 更新30

Large-scale temporal graph datasets

Large scale datasets used for evaluation in the article: `Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms` to appear in `EuroSys 2022`.

Zenodo2022-02-01 更新00

Multiple Large-Scale Datasets

该数据集的概述显示，该研究在七个大规模数据集上对15种最先进的关系抽取算法进行了性能评估，目的是为了理解复杂数据特征对关系抽取的影响。此外，这些数据集被用于分析那些影响神经关系抽取算法性能的复杂数据特征。这些数据集的规模属于大规模，所涉及的任务是关系抽取。

arXiv50