Summary of Unifying Unsupervised Graph-level Anomaly Detection and Out-of-distribution Detection: a Benchmark, by Yili Wang et al.
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
by Yili Wang, Yixin Liu, Xu Shen, Chenyu Li, Kaize Ding, Rui Miao, Ying Wang, Shirui Pan, Xin Wang
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 unified benchmark for graph-level OOD and anomaly detection is proposed, bridging the gap between independent lines of research. The new framework encompasses 35 datasets and facilitates comparison of existing methods for generalized graph-level OOD detection. Representative GLAD/GLOD methods are evaluated using multi-dimensional analyses to explore effectiveness, generalizability, robustness, and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a unified approach to detect anomalies and out-of-distribution data in graphs. By creating one benchmark that combines two separate research areas, the study makes it easier to compare different methods for detecting unusual patterns in graph-structured data. This is important because many real-world applications involve analyzing relationships between entities, like social networks or recommendation systems. |
Keywords
* Artificial intelligence * Anomaly detection
