通过Dempster-Shafer证据理论从时间序列进行受限网络重建
Cai Zhang1, Yishu Xian1, Xiao Yuan1
1School of Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
这项研究引入了一个新的框架,用于从有限的数据中重建复杂的网络拓,通过将流行病建模与Dempster-Shafer理论相结合. 该方法有效地处理不确定性,并在各种规模的网络重建中实现高精度.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 数学建模的数学建模
背景情况:
- 复杂网络模拟了各种系统中的交互.
- 由于无法观察到的连接和有限的数据,网络拓重建具有挑战性.
- 稀少的局部观测需要强大的推断方法.
研究的目的:
- 在稀疏的局部观测下开发一个用于网络拓结构重建的新框架.
- 将流行病动态与Dempster-Shafer (DS) 证据理论相结合,以改善网络推断.
- 解决数据中的不确定性和冲突,以准确确定网络结构.
主要方法:
- 一个两级的信念融合过程:节点内部融合和节点内部融合.
- 节点内部的融合聚合物SIR模拟结果生成基本概率赋值 (BPA),量化不确定性.
- 节点间融合将多个种子节点的BPA结合在一起,使用DS理论进行全球拓合成.
主要成果:
- 拟议的框架证明了网络重建的有效性和稳定性.
- 在合成和现实世界网络 (扎卡里的空手道俱乐部) 上都实现了高稳定的重建准确性.
- 该方法成功地扩展到大型网络,平均准确度为0.85.
结论:
- 双融合框架有效地处理稀疏,随机观测中的不确定性和冲突.
- 该方法为跨不同尺度和密度的网络拓重建提供了实际应用.
- 该方法为从有限的观测数据中推断网络结构提供了强大的解决方案.
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