在内容传播模型和图表机器学习中的影响问题
1University of Wisconsin-Eau Claire, Department of Mathematics, Eau Claire, Wisconsin 54701, USA.
Physical review. E
|January 21, 2026
概括
研究人员探索了在网络上传播的内容,将现有模型概括起来. 他们提出了新的影响计算和最大化问题,使用图形神经网络来预测影响概率和基准网络科学挑战.
科学领域:
- 网络科学 网络科学
- 计算社会科学 计算社会科学
- 在图表上进行机器学习.
背景情况:
- 在网络科学中,对网络传播的信息或疾病进行建模是关键的挑战.
- 影响最大化旨在找到最大化传播的种子节点,而影响计算则寻求确切的影响概率.
- 像独立级联模型这样的现有模型已经得到了很好的研究,但可能无法捕捉到所有现实世界的动态.
研究的目的:
- 引入和分析一种新的内容传播模型,其灵感来源于有限的信任模型.
- 概括独立级联模型并提出新的影响计算和最大化问题.
- 评估图形神经网络 (GNN) 以预测影响概率和评估超压问题.
主要方法:
- 引入了一种基于有限信心的新型内容传播模式.
- 证明了独立级联模型的概括性.
- 开发和应用中心性措施,用于树网络中的影响力识别.
- 训练有素的图形神经网络来预测影响概率.
主要成果:
- 拟议的内容传播模型包括独立级联模型作为一个特殊案例.
- 在树网络上的影响问题上实现了分析和计算可处理性.
- 图形神经网络显示为评估影响预测和超压现象的基准.
结论:
- 这种新的受界信任启发型模型为研究网络传染提供了更广泛的框架.
- 中心性措施为树木结构的影响问题提供了有效的解决方案.
- 在推进网络影响建模和GNN架构评估方面的研究中,GNN是一个有价值的工具.
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