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从具有拓前置的不完整图形数据对过渡矩阵的贝叶斯推理
Vincenzo Perri1, Luka V Petrović1, Ingo Scholtes2,1
1Data Analytics Group, Department of Informatics, University of Zurich, Binzmühlestrasse 14, CH-8050 Zurich, Switzerland.
本研究引入了贝叶斯方法,从不完整的交互数据推断网络过渡矩阵. 整合拓约束可以显著提高网络分析任务的准确性.
科学领域:
- 网络科学 网络科学
- 图形理论是指图形的理论.
- 机器学习是机器学习.
- 统计推断的统计推断.
背景情况:
- 网络分析依赖于随机步行模型的过渡矩阵.
- 从不完整的加权图数据来估计这些矩阵是具有挑战性的.
- 拓约束为推理提供了有价值的预先信息.
研究的目的:
- 开发一个数据效率高的贝叶斯方法来推断过渡矩阵.
- 为了利用拓约束与交互数据一起.
- 提高对不完整数据集的网络分析的准确性.
主要方法:
- 开发了一种分析性可处理的贝叶斯方法.
- 集成的重复相互作用数据与拓学先验.
- 在合成和现实世界的数据上,将方法与频率和贝叶斯基线进行了比较.
主要成果:
- 提出的方法显著提高过渡矩阵推断的准确性,特别是在有限的数据.
- 该方法甚至在部分拓约束知识的情况下也表现出稳健性.
- 过渡矩阵估计的更高准确性提高了下游任务,如集群和节点排名.
结论:
- 将拓约束与交互数据集成为网络推理提供了一种强大的方法.
- 开发的贝叶斯方法为不完整的网络数据提供了可靠和数据效率高的解决方案.
- 这项工作对网络系统的跨学科数据驱动分析具有实际意义.
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