一个可扩展的适应性策略,通过以为基础的元启发来最大限度地影响时间社会网络
Linian Liu1, Binrong Huang2, Shouliang Lai3
1Hanyang University, Seoul, 04763, Korea.
Scientific reports
|November 20, 2025
概括
自适应动态算法 (ADVA) 通过适应网络变化来增强动态社交网络中的影响力. 虽然有效,但它的计算复杂性导致更长的执行时间,需要进一步优化.
科学领域:
- 社交网络分析 社交网络分析
- 计算智能是一种计算智能.
- 算法设计 算法设计
背景情况:
- 社交网络对于信息传播和论形成至关重要.
- 在动态,大规模网络中识别有影响力的个人是具有挑战性的.
- 以前的方法往往忽视了网络动态和可扩展性.
研究的目的:
- 介绍适应性动态算法 (ADVA),用于动态社交网络中的影响力最大化.
- 通过适应时间网络变化,实现勘探和开发之间的最佳平衡.
- 解决大型和不断变化的网络结构中的可扩展性挑战.
主要方法:
- 开发了ADVA,一个新的Meta-Heuristic算法.
- 实施适应性技术,根据网络变化 (边缘密度,节点连接) 调整搜索方法.
- 利用修剪和索引来管理动态数据的计算复杂性.
主要成果:
- 在Stack Overflow上,ADVA提高了15%的影响力透率,在Wiki Talk上提高了20%.
- 在大型网络评估中证明了可扩展性.
- 由于计算复杂性,观察到延长的执行时间 (例如,在Stack Overflow上种子大小为60的4800s).
结论:
- 对于动态的社交网络,ADVA在影响力最大化方面提供了显著的改进.
- 算法的自适应性和多阶段优化有助于其有效性.
- 需要进一步的研究来提高ADVA的计算效率.
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