两个阶段的多目标进化算法,用于重叠的社区发现
Lei Cai1,2, Jincheng Zhou1, Dan Wang3
1Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, China.
PeerJ. Computer science
|August 15, 2024
概括
这项研究引入了一种新的两阶段进化算法,用于复杂网络中的重叠社区发现. 该方法准确地识别重叠的社区,改进网络分析和建模能力.
科学领域:
- 复杂的网络 复杂的网络
- 网络科学 网络科学
- 数据挖掘 数据挖掘
背景情况:
- 社区结构是具有广泛应用的复杂网络的关键特征.
- 社交网络中的个人通常同时属于多个社区.
- 重叠的社区发现对于准确的网络建模至关重要.
研究的目的:
- 为重叠社区发现提出一个两阶段的多目标进化算法.
- 准确地识别网络中属于多个社区的个人.
主要方法:
- 一个两阶段的进化算法,结合了非重叠的社区划分和模糊的集群.
- 基于节点度和基因组矩阵演变的初始化为第一阶段.
- 使用进化计算和第二阶段的反模型进行模糊值优化.
主要成果:
- 拟议的算法在合成和现实世界数据集上表现出最佳性能.
- 统计结果显示,与现有的代表性算法相比,性能优越.
- 算法有效地找到合理的重叠节点.
结论:
- 开发的算法为重叠的社区发现问题提供了有效的解决方案.
- 这种方法增强了对具有多种关系的复杂网络结构的理解和建模.
- 该算法的最佳性能验证了其在网络分析中的有效性.
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