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Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks

arXiv 2312.00232 ↗First posted 30 Nov 2023cs.LG cs.AI stat.MLcited by 0

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Abstract (arXiv)

Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available. However, it often does not take uncertainty estimation into account. We show that a variational Bayesian neural network approach can be used to improve not only the uncertainty estimates but also the downstream performance on semi-supervised node-classification tasks. Moreover, we propose a new measure of uncertainty for contrastive learning, that is based on the disagreement in likelihood due to different positive samples.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 58 of its 74 references that are in the map. First posted on arXiv 30 Nov 2023.

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References · 58

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38 more
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