ARISE:通过子结构意识在指定的网络上检测图形异常
IEEE transactions on neural networks and learning systems
|September 22, 2023
概括
本研究介绍了ARISE,这是一个用于在赋值网络中检测图形异常的新型框架. 通过识别密集的子结构和利用图形对比学习来提高准确度,ARISE提高了检测.
科学领域:
- 数据挖掘 数据挖掘
- 机器学习 机器学习
- 网络分析 网络分析
背景情况:
- 在属性网络上检测图形异常是一个不断增长的研究领域.
- 现有的方法往往忽略了密集的基层结构中的集体异常行为.
- 区分属性和拓异常仍然是一个挑战.
研究的目的:
- 提出一个新的框架,ARISE (通过子结构意识识别异常),用于在归属网络上检测图形异常.
- 通过识别密集的亚结构中的集体模式来改进拓异常检测.
- 为了同时检测拓和属性异常.
主要方法:
- ARISE使用一个区域提案模块来识别高密度的基础结构作为可疑的区域.
- 它根据子结构中的平均节点对相似性计算拓异常度.
- 集成了一个图形对比学习方案来改进节点属性嵌入和检测属性异常.
主要成果:
- 与最先进的归因网络异常检测 (ANAD) 算法相比,ARISE在检测性能方面取得了显著的改进.
- 该框架在曲线下的面积 (AUC) 中获得了高达7.30%的收益.
- 精度回忆曲线 (AUPRC) 下面的面积也得到了显著改善,达到17.46%.
结论:
- 在赋值网络中,ARISE有效地检测到拓和属性异常.
- 专注于子结构意识显著提高了图形异常检测能力.
- 拟议的框架在归因网络异常检测方面提供了一个有希望的进步.
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