基于图传输和图解耦的跨域图异常检测

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Changqin Huang, Xinxing Shi, Chengling Gao, Qintai Hu, Xiaodi Huang, Qionghao Huang, Ali Anaissi
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引用次数: 0

摘要

跨域图异常检测(CD-GAD)是一种很有前途的任务,它利用标记源图的知识来指导未标记目标图的异常检测。CD-GAD根据异常在源图和目标图中的存在情况,将异常分为独特的异常和常见的异常。然而,现有的模型往往不能充分挖掘目标图的领域唯一知识来检测独特的异常。此外,它们往往只关注节点级差异,而忽略了为常见异常检测提供补充信息的结构级差异。为了解决这些问题,我们提出了一种新的方法——基于图传输和图解耦的合成图异常检测(GTGD),该方法可以有效地检测目标图中的常见和唯一异常。具体来说,我们的方法通过解耦公共和唯一特征的重建图来确保更深层次的领域唯一知识学习。此外,我们通过将节点和边缘信息从源图传递到目标图,同时考虑节点级和结构级的差异,从而实现全面的领域公共知识表示。使用共同和独特的特征来检测异常,并将其综合得分作为最终结果。大量的实验证明了我们的方法的有效性,与最先进的方法相比,AUC-PR的平均性能提高了12.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Cross-Domain Graph Anomaly Detection via Graph Transfer and Graph Decouple

Cross-Domain Graph Anomaly Detection via Graph Transfer and Graph Decouple

Cross-Domain Graph Anomaly Detection via Graph Transfer and Graph Decouple

Cross-Domain Graph Anomaly Detection via Graph Transfer and Graph Decouple

Cross-domain graph anomaly detection (CD-GAD) is a promising task that leverages knowledge from a labelled source graph to guide anomaly detection on an unlabelled target graph. CD-GAD classifies anomalies as unique or common based on their presence in both the source and target graphs. However, existing models often fail to fully explore domain-unique knowledge of the target graph for detecting unique anomalies. Additionally, they tend to focus solely on node-level differences, overlooking structural-level differences that provide complementary information for common anomaly detection. To address these issues, we propose a novel method, Synthetic Graph Anomaly Detection via Graph Transfer and Graph Decouple (GTGD), which effectively detects common and unique anomalies in the target graph. Specifically, our approach ensures deeper learning of domain-unique knowledge by decoupling the reconstruction graphs of common and unique features. Moreover, we simultaneously consider node-level and structural-level differences by transferring node and edge information from the source graph to the target graph, enabling comprehensive domain-common knowledge representation. Anomalies are detected using both common and unique features, with their synthetic score serving as the final result. Extensive experiments demonstrate the effectiveness of our approach, improving an average performance by 12.6 % $\%$ on the AUC-PR compared to state-of-the-art methods.

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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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