使用NOME算法在动态网络社区发现中提高时间流性和快照质量
Lei Cai1, Jincheng Zhou2, Dan Wang3
1State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.
PeerJ. Computer science
|August 7, 2023
概括
我们介绍了NOME,这是一个新的动态社区发现算法,使用节点占用分配和进化聚类. 这种方法准确地识别网络社区,改善分类,并揭示动态网络变化,具有良好的质量和速度平衡.
科学领域:
- 计算机科学 计算机科学
- 网络科学 网络科学
- 数据挖掘 数据挖掘
背景情况:
- 动态社区发现对于分析不断发展的网络结构和分类具有类似属性的个体至关重要.
- 准确的分类有助于过所需结果并了解动态网络变化.
- 现有的方法往往难以在动态网络中平衡社区检测准确性和计算效率.
研究的目的:
- 提出一种新的动态社区发现算法NOME,以提高准确性和效率.
- 利用节点占用分配和多目标进化聚类来改进社区检测.
- 提供一种在动态网络分析中平衡快照质量与时间成本的方法.
主要方法:
- 诺姆利用MOEA/D框架实现多目标进化聚类,分解模块化和规范化的相互信息目标.
- 一个基于Physarum的网络模型初始化了种群,进化是由基因组矩阵交叉和突变操作驱动的.
- 引入了一种新的节点占用分配策略,专注于边界节点,以提高社区划分的真实性.
主要成果:
- 在合成和真实数据集上的比较实验证明了NOME对代表性动态社区检测算法的有效性.
- 在快照质量和计算时间成本之间,NOME实现了卓越的平衡.
- 节点占用分配策略提高了社区划分的准确性,特别是在社区边界的节点.
结论:
- 在复杂的网络中,NOME为动态社区发现提供了有效和高效的解决方案.
- 提出的节点占用分配策略显著提高了社区划分的真实性.
- 对于需要分析不断演变的网络结构并进行准确的分类的研究人员来说,NOME提供了一个有价值的工具.
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