AHD-SLE:超图对称线展开上的异常超edge 检测

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Yingle Li, Hongtao Yu, Haitao Li, Fei Pan, Shuxin Liu
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引用次数: 0

摘要

图异常检测旨在识别图结构数据中的异常模式或结构。现有研究大多侧重于具有成对关系的普通图中的异常节点。然而,复杂的现实世界系统往往涉及超越成对关系的关系,而对超图异常检测,尤其是异常超边检测的关注不够。现有的一些研究超图的方法涉及将超图转化为普通图进行学习,这会导致高阶信息的丢失,从而导致检测性能低下。我们提出了一种对称线展开异常超图检测(AHD-SLE)的新方法。超图的 SLE 是具有成对关系的普通图,可以反映射到超图,因此 SLE 能够保留超图的高阶信息。AHD-SLE 首先将超图映射到 SLE,然后在 SLE 中通过图卷积网络(GCN)聚合信息。然后,通过反映射操作获得超边缘嵌入表示。最后,设计了一个异常函数,利用超边缘嵌入表示检测异常超边缘。在五种不同类型的真实超图数据集上进行的实验表明,AHD-SLE 在接收器运行特征曲线下面积(AUC)和召回指标方面优于基线算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AHD-SLE: Anomalous Hyperedge Detection on Hypergraph Symmetric Line Expansion
Graph anomaly detection aims to identify unusual patterns or structures in graph-structured data. Most existing research focuses on anomalous nodes in ordinary graphs with pairwise relationships. However, complex real-world systems often involve relationships that go beyond pairwise relationships, and insufficient attention is paid to hypergraph anomaly detection, especially anomalous hyperedge detection. Some existing methods for researching hypergraphs involve transforming hypergraphs into ordinary graphs for learning, which can result in poor detection performance due to the loss of high-order information. We propose a new method for Anomalous Hyperedge Detection on Symmetric Line Expansion (AHD-SLE). The SLE of a hypergraph is an ordinary graph with pairwise relationships and can be backmapped to the hypergraph, so the SLE is able to preserve the higher-order information of the hypergraph. The AHD-SLE first maps the hypergraph to the SLE; then, the information is aggregated by Graph Convolutional Networks (GCNs) in the SLE. After that, the hyperedge embedding representation is obtained through a backmapping operation. Finally, an anomaly function is designed to detect anomalous hyperedges using the hyperedge embedding representation. Experiments on five different types of real hypergraph datasets show that AHD-SLE outperforms the baseline algorithm in terms of Area Under the receiver operating characteristic Curve(AUC) and Recall metrics.
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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