GATCF:图表注意力协作过用于在BaaS中选择可靠的区块链服务.
Yuxiang Zeng1, Jianlong Xu1, Zhuohua Zhang1
1College of Engineering, Shantou University, Shantou 515063, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
由于数据稀疏性,选择可靠的区块链对手是具有挑战性的. 我们的图表注意力协作过 (GATCF) 模型有效地解决了这一问题,通过利用图表注意力和协作过来改进同行选择.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
背景情况:
- 区块链即服务 (BaaS) 简化了区块链应用程序的开发.
- 许多具有重叠特征的 BaaS 选项使可靠的同行选择变得复杂.
- 数据稀疏性是识别可信的区块链同行的一个重大挑战.
研究的目的:
- 为选择可靠的区块链同行提出一个新的模型.
- 为解决区块链即服务 (BaaS) 环境中的数据稀疏性问题.
主要方法:
- 开发了一个基于协作过的矩阵完成模型,名为图形注意力协作过 (GATCF).
- 集成图表注意力机制,以捕捉同行互动和依赖.
- 应用矩阵完成技术来恢复同行选择中缺失的数据点.
主要成果:
- GATCF模型证明了数据矩阵中缺失值的有效恢复.
- 在大型数据集上的实验结果证实了该模型的卓越性能.
- 与现有方法相比,实现了更高的恢复准确性,以减轻数据稀疏性.
结论:
- 在BaaS的同行选择中,GATCF有效地减轻了数据稀疏性的挑战.
- 拟议的模型提高了选择可信的区块链对象的可靠性.
- 图表注意力和协作过集成为分散系统提供了强大的解决方案.
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