GEDet: Detecting Erroneous Nodes with A Few Examples

Conference · Mon Aug 16 04:00:00 EDT 2021

DOI:https://doi.org/10.14778/3476311.3476367· OSTI ID:1837569

Guan, Sheng ^[1]; Ma, Hanchao ^[2]; Choudhury, Sutanay ^[3]; Wu, Yinghui ^[4]

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1. CASE WESTERN RESERVE UNIVERSITY
2. Case Western Reserve University
3. BATTELLE (PACIFIC NW LAB)
4. CASE WESTERN RESERVE UNIVERSITY (JA)

Detecting nodes with erroneous values in real-world graphs re- mains challenging due to the lack of examples and various error scenarios. We demonstrate GEDet, an error detection engine that can detect erroneous nodes in graphs with a few examples. The GEDet framework tackles error detection as a few-shot node classification problem. We invite the attendees to experience the following unique features. (1) Few-shot detection. Users only need to provide a few examples of erroneous nodes to perform error detection with GEDet. GEDet achieves desirable accuracy with (a) a graph augmentation module, which automatically generates synthetic examples to learn the classifier, and (b) an adversarial detection module, which improves classifiers to better distinguish erroneous nodes from both cleaned nodes and synthetic examples. We show that GEDet significantly improves the state-of-the-art error detection methods. (2) Diverse error scenarios. GEDet profiles data errors with a built-in library of transformation functions from correct values to errors. Users can also easily “plug in” new error types or examples. (3) User-centric detection. GEDet supports (a) an active learning mode to engage users to verify detected results, and adapts the error detection process accordingly; and (b) visual interfaces to interpret and track detected errors.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1837569
• Report Number(s): PNNL-SA-160866
• Country of Publication: United States
• Language: English