Conformal load prediction with transductive graph autoencoders
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DOI: 10.1007/s10994-024-06713-w
Abstract: Predicting edge weights on graphs has various applications, from transportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weight prediction with guaranteed coverage. We leverage conformal prediction to… read more here.
Keywords: transductive graph; load prediction; conformal load; graph autoencoders ... See more keywords
Efficient Spiking Variational Graph Autoencoders for Unsupervised Graph Representation Learning Tasks
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DOI: 10.1109/mis.2024.3391937
Abstract: Variational graph autoencoders (VGAEs) are popular artificial neural network (ANN)-based models for unsupervised graph representation learning tasks, including link prediction and graph generation, which are critical in many real-world applications. Despite the promising results of… read more here.
Keywords: learning tasks; graph autoencoders; graph representation; variational graph ... See more keywords