ActiveReach:一个主动学习框架,用于大致的可访问性查询,在大规模图表中回答
Zohreh Raghebi1, Farnoush Banaei-Kashani1
1Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, United States.
Frontiers in big data
|December 4, 2024
概括
ActiveReach使用节点属性和主动学习来学习近似的图形可访问性. 这个框架平衡了查询速度和对大型属性图的准确性.
科学领域:
- 图形理论 图形理论
- 机器学习 机器学习
- 数据库系统 数据库系统
背景情况:
- 传统的图表可访问性查询提供了准确的答案,但在大型图表上速度很慢.
- 大致可访问性查询提供了查询时间和准确性之间的权衡.
- 现有的方法缺乏有效的解决方案来学习在大型赋值图表上的近似可访问性.
研究的目的:
- 提出ActiveReach,一个用于学习索引结构的新框架,以回答近似可访问性查询.
- 开发一种有效平衡查询时间和准确度的方法,用于可访问性分析.
- 解决所有训练数据在大型图形中计算可达性的计算挑战.
主要方法:
- 一个双相框架,重点是将节点嵌入到可访问空间中.
- 第一个阶段:使用节点属性和位置信息创建可访问性意识的节点嵌入.
- 第二阶段:纳入新的属性和可访问性标签,使用主动学习来选择可访问性计算的数据并增强预测.
主要成果:
- ActiveReach有效地学习大规模归因图的近似可访问性.
- 积极学习组件有效地选择数据,提高可访问性预测性能.
- 实验研究证明了ActiveReach框架内各组件的有效性.
结论:
- ActiveReach提供了一种有效的解决方案,用于大规模的属性图表上的近似图表可访问性查询.
- 该框架通过学习索引结构成功平衡了查询效率和准确性.
- 积极学习策略对于管理计算成本,同时保持预测性能至关重要.
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