Summary of Deep Graph Anomaly Detection: a Survey and New Perspectives, by Hezhe Qiao et al.
Deep Graph Anomaly Detection: A Survey and New Perspectives
by Hezhe Qiao, Hanghang Tong, Bo An, Irwin King, Charu Aggarwal, Guansong Pang
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive review of deep learning approaches for graph anomaly detection (GAD) is presented, highlighting the strengths and limitations of existing methods in tackling complex GAD problems. The study focuses on three novel perspectives: methodology, including GNN backbone design, proxy task design for GAD, and graph anomaly measures; GNN-based GAD models are shown to be effective in capturing complex structure and node attributes in graph data. A taxonomy of 13 fine-grained method categories is proposed to provide in-depth insights into model designs and capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs can be unusual or anomalous, which is important for many applications. Deep learning approaches, specifically graph neural networks (GNNs), are great at finding these anomalies because they understand complex structures and node attributes in graphs. This paper looks at all the different methods that have been proposed for GAD and summarizes them to help us find effective solutions for open problems. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » Gnn
