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超意识的自适应式启发式算法:一种基于超图的扩散扩散中的种子选择的新方法

Dandan Zhao¹, Yongqi Zhang¹, Bo Zhang²

¹School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang 321004, China.

Chaos (Woodbury, N.Y.)

|November 3, 2025

概括

本研究介绍了复杂网络的基于超图的影响最大化 (IM) 算法. 一种新的超度缩放方法优化了扩散性能和效率,超过了基线.

科学领域:

网络科学网络科学
计算社会科学计算社会科学
流行病学流行病学

背景情况:

影响最大化 (IM) 对于网络分析至关重要,但传统的图形模型在高阶交互方面存在困难.
超图为复杂系统所必需的多节点关系提供了一个更具表达性的框架.

研究的目的:

用接触过程动态的易受感染 (SI) 模型研究对超图的影响最大化.
开发和评估新的超意识适应式启发式算法,以改善扩散.

主要方法:

提出了四个超意识的自适应式启发式算法,利用节点度和超度.
分析了种子节点对第一级和第二级邻居的影响.
对现实世界和合成超图进行了不同异质性的实验.

主要成果:

建议的算法在扩散效率方面始终超过了基线方法,特别是在有限的种子预算下.
超度缩放的第一阶邻居减少算法证明了性能和效率之间的最佳平衡.
在最大效率方面实现了高达30.29%的改进,在有限的预算下提高了63.42%.

结论:

基于超图的IM方法在复杂交互方面优于传统的图形方法.
开发的超意识算法在扩散效率和计算效率方面提供了显著的改进.
超级度缩放的第一阶邻居减少算法是实际影响最大化的一个有希望的策略.

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