贝恩算法:通过模块化的精确和近似优化来检测网络中的社区.
Samin Aref1, Mahdi Mostajabdaveh2, Hriday Chheda1
1Department of Mechanical and Industrial Engineering, <a href="https://ror.org/03dbr7087">University of Toronto</a>, Toronto, Canada M5S 3G8.
Physical review. E
|November 20, 2024
概括
拜安算法通过最大化模块化来提供最佳的社区检测,在小网络的准确性和稳定性方面表现优于其他29种方法. 它的Python实现是公开可用的.
科学领域:
- 网络科学 网络科学
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
背景情况:
- 在网络分析中,社区检测至关重要,模块化最大化是常见的,但往往不是最佳的方法.
- 现有的方法难以找到全球最佳分区,这使得这个领域未得到充分探索.
- 在各种科学领域中,对准确和稳定的社区检测算法的需求至关重要.
研究的目的:
- 介绍和评估Bayan算法用于网络社区检测.
- 将Bayan的性能与30种现有方法进行比较,重点关注最佳性和近似性保证.
- 为了证明最大模块化隔壁在检索植栽结构中的优势.
主要方法:
- 开发了Bayan算法,它保证了全球模块化最大化或近似.
- 通过使用标准基准,比较了Bayan与在多样化,结构多样化的网络上的30个社区检测算法.
- 根据包括描述长度,覆盖范围,性能,平均导电量和良好集群性在内的指标来评估分区.
主要成果:
- 由Bayan生成的最大模块化分区在检索植入分区时表现出独特的准确性和稳定性.
- 拜扬的分区在多个性能指标中表现优于其他29个算法.
- 贝恩高效地实现了最佳性或近最佳性,在模块化最大化方面表现优于商业和开源解决方案.
结论:
- 拜安算法是小型网络中社区检测的可靠和准确方法.
- 最大模块化分区比启发式方法具有显著的优势.
- "bayanpy" Python 实现的可用性促进了更广泛的采用和研究.
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