RIA uses adversarial exploration of counterfactual graph environments via label-invariant augmentations to improve OoD generalization in graph classification tasks.
Out-of-distribution generalization on graphs: A survey.arXiv preprint arXiv:2202.07987
2 Pith papers cite this work, alongside 26 external citations. Polarity classification is still indexing.
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A probabilistic graphical model framework with graph neural network inference computes Bayesian posteriors for discrete structural states, claimed to match traditional Bayesian results while scaling to high-dimensional problems via topology-informed learning and scale-adaptive training.
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Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization
RIA uses adversarial exploration of counterfactual graph environments via label-invariant augmentations to improve OoD generalization in graph classification tasks.
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Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
A probabilistic graphical model framework with graph neural network inference computes Bayesian posteriors for discrete structural states, claimed to match traditional Bayesian results while scaling to high-dimensional problems via topology-informed learning and scale-adaptive training.