In this paper, we explore different approaches to anomaly detection on dynamic knowledge graphs, specifically in a microservices environment for Kubernetes applications. Our approach explores three dynamic knowledge graph representations: sequential data, one-hop graph structure, and two-hop graph structure, with each representation incorporating increasingly complex structural information. Each phase includes different machine learning and deep learning models. We empirically analyse their performance and propose an approach based on ensemble learning of these models. Our approach significantly outperforms the baseline on the ISWC 2024 Dynamic Knowledge Graph Anomaly Detection dataset, providing a robust solution for anomaly detection in dynamic complex data.

翻译：本文探讨了动态知识图谱异常检测的不同方法，特别针对Kubernetes应用微服务环境。我们的方法研究了三种动态知识图谱表示形式：序列数据、单跳图结构和双跳图结构，每种表示形式都融入了日益复杂的结构信息。每个阶段包含不同的机器学习和深度学习模型。我们通过实证分析评估了它们的性能，并提出了一种基于这些模型集成学习的方法。我们的方法在ISWC 2024动态知识图谱异常检测数据集上显著优于基线，为动态复杂数据中的异常检测提供了稳健的解决方案。