TaCD:基于多视图模块化的团队意识社区检测
Chengzhou Fu1, Feiyi Tang2, Lingzhi Hu3,4
1College of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一个新的算法,团队意识到社区检测 (TaCD),用于社交网络. 在社区检测任务中,TaCD有效地结合了团队信息,超过了现有的方法.
科学领域:
- 网络科学 网络科学
- 社交网络分析分析
- 数据挖掘是一种数据挖掘.
背景情况:
- 在网络科学中,社区检测至关重要.
- 现有的方法往往忽略了社交网络中的重要团队信息.
- 团队结构显著影响社区的形成和演变.
研究的目的:
- 提出一种新的社区检测算法,集成团队信息.
- 开发一个包含用户交互和团队结构的多视图网络模型.
- 引入一个新的数据集来评估团队意识的社区检测.
主要方法:
- 构建了一个具有用户交互 (用户视图) 和团队信息 (团队视图) 的多视图网络.
- 为了测量一致性,利用了对交叉视图合的Jaccard相似性.
- 采用多视图模块化和优化通用卢瓦恩方法.
主要成果:
- 拟议的团队意识社区检测 (TaCD) 算法有效地识别社区.
- 与基准数据集上的现有算法相比,TaCD显示出更高的性能.
- 新的SCHOLAT数据集促进了对团队意识社区检测的研究.
结论:
- 整合团队信息可以提高社交网络中社区检测的准确性.
- 塔CD为揭示复杂的社区结构提供了一个强大的框架.
- 开发的算法和数据集对网络科学领域做出了重大贡献.
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