神经子图通过局部更新和单调学习计算流图
Zhen Xie1, Wenzhe Hou1, Feiyang Wu1
1Laboratory for Big Data and Decision, National University of Defense Technology, ChangSha, HuNan, China.
PloS one
|October 23, 2025
概括
本研究介绍了StreamSC,这是一个基于学习的新型框架,用于动态流图中的子图计数. StreamSC有效地估计了子图数,克服了在不断变化的图形数据中#P-complete的挑战.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 图形理论 图形理论
背景情况:
- 图形是代表复杂关系的基本数据结构.
- 流图有效地处理动态演变的图形数据.
- 在流图中计数子图是计算上具有挑战性的 (#P-complete).
研究的目的:
- 介绍StreamSC,这是一个用于流图中子图计数的新框架.
- 解决动态图中子图计数的计算挑战.
- 提出了第一个基于学习的方法,用于在流图中计算子图.
主要方法:
- 开发了基于学习的框架StreamSC.
- 集成的创新来处理动态图形变化 (边缘插入/删除).
- 在五个现实世界的图表上评估了性能.
主要成果:
- 在子图计数方面,StreamSC表现出高精度.
- 流SC实现了显著的效率提升.
- 该框架有效地处理动态图形演变.
结论:
- 流SC提供了一个优先解决方案,用于在流图上计算子图.
- 基于学习的方法有效地应对#P-complete挑战.
- StreamSC提供了一种高效准确的方法来分析不断变化的图形数据.
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