基于图形嵌入的标签传播,用于社交网络中的社区检测.
Shyam Sundar Meena1, Prakash Chandra Sharma2, Yadvendra Pratap Singh3
1Shri Vaishnav Vidyapeeth Vishwavidyalaya, Ujjain Road, Indore, Madhya Pradesh, 453111, India.
Scientific reports
|November 25, 2025
概括
我们介绍了基于嵌入的标签传播 (ELP),这是一种用于网络社区检测的新算法. 通过集成节点嵌入,ELP改进了标签传播算法 (LPA),从而在复杂的网络中实现更准确的社区结构和更高的模块化.
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:
- 现实世界的网络展示了社区结构,这对于理解他们的组织至关重要.
- 传统的社区检测方法,如标签传播算法 (LPA),在计算上是高效的,但往往缺乏准确性.
- LPA的局限性源于它在标签传播过程中天真地选择邻居,影响模块化和结构完整性.
研究的目的:
- 开发一个改进的社区检测算法,解决传统LPA的局限性.
- 提高复杂网络中社区检测的准确性和结构理解.
- 创建一种混合方法,将本地连接与全球结构信息相结合.
主要方法:
- 开发了基于嵌入的标签传播 (ELP),这是一个混合算法,将LPA与节点嵌入技术集成在一起.
- 在ELP的更新步骤中,考虑了当地的社区信息和基于嵌入的邻居选择相似之处.
- 在基准数据集 (空手道俱乐部,海豚,足球,Polbooks) 和LFR合成网络上评估ELP,将性能与LPA和其他既有算法进行比较.
主要成果:
- 在模块化,规范化相互信息 (NMI) 和F1得分 (NF1) 方面,ELP始终优于LPA和其他算法.
- 该算法展示了可扩展性,在大型和复杂的网络上有效执行.
- 经验证实证实了ELP在确定准确的社区结构方面的卓越表现.
结论:
- 基于嵌入的标签传播 (ELP) 在网络社区检测方面取得了重大进展.
- ELP提供了一种强大而有效的方法,用于在现实世界和合成网络中发现社区结构.
- 节点嵌入的集成提高了社区检测算法的准确性和稳定性.
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