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Preprint
Are Graph Neural Networks Miscalibrated?
arXiv
05/06/2019
Abstract
Graph Neural Networks (GNNs) have proven to be successful in many classification tasks, outperforming previous state-of-the-art methods in terms of accuracy. However, accuracy alone is not enough for high-stakes decision making. Decision makers want to know the likelihood that a specific GNN prediction is correct. For this purpose, obtaining calibrated models is essential. In this work, we perform an empirical evaluation of the calibration of state-of-the-art GNNs on multiple datasets. Our experiments show that GNNs can be calibrated in some datasets but also badly miscalibrated in others, and that state-of-the-art calibration methods are helpful but do not fix the problem.
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Details
- Title
- Are Graph Neural Networks Miscalibrated?
- Resource Type
- Preprint
- Publisher
- arXiv; arxiv.org
- Format
- link
- Copyright
- © the author(s)
- Identifiers
- 99381512042406600
- Academic Unit
- Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English