Summary of Guarding Graph Neural Networks For Unsupervised Graph Anomaly Detection, by Yuanchen Bei et al.
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
by Yuanchen Bei, Sheng Zhou, Jinke Shi, Yao Ma, Haishuai Wang, Jiajun Bu
First submitted to arxiv on: 25 Apr 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 Unsupervised graph anomaly detection is a crucial task in various applications, where the goal is to identify rare patterns deviating from the majority in an unlabeled graph. Recent advances have utilized Graph Neural Networks (GNNs) to learn node representations by aggregating neighborhood information, assuming consistency between nodes and their neighborhoods. However, this assumption can be disrupted by graph anomalies, leading to sub-optimal results. The proposed Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection (G3AD) framework addresses this issue by introducing auxiliary networks with correlation constraints and an adaptive caching module to guard GNNs from inconsistent information encoding and reconstructing observed data containing anomalies. Experimental results demonstrate that G3AD outperforms seventeen state-of-the-art methods on both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big network of people, and some of them are doing something weird or different from the others. You want to find those unusual people without knowing what they’re doing beforehand. This is called graph anomaly detection, and it’s important for many real-life applications. Recently, scientists used special computer models called Graph Neural Networks (GNNs) to learn about these networks. But GNNs can be tricked by the weird behavior of some nodes in the network. To fix this problem, we proposed a new framework that helps GNNs work better and ignore the weird data. Our experiments showed that our method works better than many other methods on both fake and real datasets. |
Keywords
» Artificial intelligence » Anomaly detection » Unsupervised
