在属性网络中用于异常检测的统一表示和评分框架,重点是结构一致性和属性完整性
Wasim Khan1, Nadhem Ebrahim2, Mohammed Alsaadi3
1Symbiosis Institute of Technology, PUNE, Symbiosis International (Deemed University), Pune, India. wasim.khan@sitpune.edu.in.
Scientific reports
|October 14, 2025
概括
本研究引入了一种混合框架,用于在属性网络中检测异常,有效地结合多种学习方法. 拟议的方法通过整合结构和属性信息,显著提高了异常的检测.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 网络分析 网络分析
背景情况:
- 在赋值网络中检测异常对于安全和欺诈检测至关重要.
- 现有的方法在结构和属性两方面都存在异常.
- 单一的学习模式限制了捕捉各种异常行为.
研究的目的:
- 为分配网络异常检测提出一个全面的混合框架.
- 整合多个学习组件,以进行可靠的异常识别.
- 改善因结构和属性不一致而产生的异常的检测.
主要方法:
- 一个混合框架,集成图形结构重建和属性重建.
- 社区意识的对比学习对歧视性代表性.
- 基于社区的改进的相似感知异常得分.
- 在六个基准数据集上进行评估:BlogCatalog,Flickr,ACM,Cora,Citeseer和Pubmed.
主要成果:
- 拟议的框架显著超过了最先进的基线.
- 在AUC和AUPR评估指标中取得了卓越的表现.
- 废弃性研究证实了每个模块的贡献.
- 参数灵敏度分析表明框架的稳定性.
结论:
- 混合框架有效地检测到复杂的属性图中的各种异常.
- 统一的设计捕捉了全球结构模式和本地语义一致性.
- 该方法在归因网络异常检测方面表现出有效性和通用性.
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