What is it about?
This paper offers a comprehensive review of 53 contemporary anomaly detection methods in dynamic knowledge graphs, covering developments from 2016 to 2023. It delves into next-generation technologies, including quantum graph neural networks (GNNs), providing a detailed comparison of existing techniques and charting future research directions in this quickly advancing field.
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Why is it important?
Our survey paper is important as it provides the latest anomaly detection methods in dynamic graphs, offering a roadmap for future advancements. It bridges knowledge gaps and catalyzes innovation in a field critical for enhancing the security and efficiency of complex graph network systems.
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This page is a summary of: Anomaly Detection in Dynamic Graphs: A Comprehensive Survey, ACM Transactions on Knowledge Discovery from Data, May 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3669906.
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