区块纠正模块化,用于社区检测
Hasti Narimanzadeh1, Takayuki Hiraoka1, Mikko Kivelä1
1Aalto University, Department of Computer Science, 00076 Espoo, Finland.
Physical review. E
|September 16, 2025
概括
我们引入了区块纠正的模块化,以揭示复杂网络中隐藏的社区结构. 这种方法有效地揭示了被已知的属性掩盖的社区,在合成和现实世界的数据上表现优于现有的技术.
科学领域:
- 网络科学 网络科学
- 数据挖掘 数据挖掘
- 复杂系统分析 复杂系统分析
背景情况:
- 复杂网络中的社区结构经常受到已知和未知的节点属性的影响.
- 区分由不同属性类型驱动的社区结构对于准确的网络分析至关重要.
- 当现有的方法受到混块结构的影响时,可能无法识别底层社区.
研究的目的:
- 开发一种新的模块化测量方法,即区块纠正的模块化,以识别由已知的属性掩盖的社区结构.
- 通过分析和经验证明区块纠正模块化在揭示隐藏的社区结构方面的有效性.
- 提供高效的算法来最大限度地提高拟议的模块化,并将其应用于现实世界的网络.
主要方法:
- 提出一个区块纠正的模块化,以抵消网络内的现有区块结构.
- 在一个简单的网络模型中分析模块化效率的推导.
- 开发光谱和卢温灵感的算法,以实现模块化最大化.
- 在合成网络模型和真实世界引用网络 (OpenAlex数据) 上进行验证.
主要成果:
- 区块纠正的模块化成功地识别了由合成模型中未知的属性驱动的社区结构.
- 拟议的方法优于使用不同零模型的现有技术.
- 有效的算法在最大限度地实现块校正的模块化方面表现出强的性能.
- 对引用网络的应用纠正了时间引用模式,揭示了潜在的研究社区.
结论:
- 区块纠正模块化是一种强大的方法,用于揭示复杂网络中潜在的社区结构.
- 开发的算法为应用这种新型模块化措施提供了有效的手段.
- 这种方法通过考虑时间动态等混因素来增强现实世界网络的分析.
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