通过优化节点和社区之间的交互关系来检测复杂网络中的本地社区
Shenglong Wang1, Jing Yang1, Xiaoyu Ding2
1College of Computer Science and Technology, Harbin Engineering University, Harbin, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究介绍了OIRLCD,一种新的当地社区检测算法. 它改进了种子选择,并使用相似性指数来优化节点-社区交互,实现准确和高效的社区检测.
科学领域:
- 网络科学 网络科学
- 数据挖掘 数据挖掘
- 计算社会科学 计算社会科学
背景情况:
- 当地社区检测算法旨在识别特定节点周围的最佳社区.
- 现有的方法经常面临种子选择和准确测量节点-社区关系的挑战.
研究的目的:
- 开发和评估一种新的当地社区检测算法,OIRLCD.
- 为了解决种子偏差问题并增强节点-社区相互作用的测量.
主要方法:
- 引入了一种改进的种子选择技术.
- 利用各种相似度指数来量化节点-社区交互关系.
- 开发基于不同相似度指数的算法来优化这些关系.
- 在现实世界和人工网络上进行了比较实验.
主要成果:
- 该OIRLCD算法展示了准确和高效的社区检测能力.
- 使用节点相似性优化交互关系是有效的.
- 选择相似度指数显著影响关系优化算法的性能.
结论:
- 拟议的OIRLCD算法为当地社区检测提供了一种有效的方法.
- 通过适当的相似性指数优化交互关系对于准确的社区发现至关重要.
- 与各种网络类型的现有方法相比,该算法表现出强的性能.
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