Uncertainty Quantification in Node Classification
Modern neural networks are widely applied in a variety of learning tasks due to their exceptional performance, but fail to express uncertainty about predictions. For example, if a neural network is trained to predict whether an image contains a cat or a dog and is given an elephant as input, it will not admit that it is unsure. With a relatively high probability the machine learning model will instead still choose cat or dog. For high-risk domains like healthcare and autonomous driving this is not the best approach. In these areas, the cost and damage caused by overconfident or underconfident predictions can be catastrophic.