623 citations · 2.1k across the 30 of their papers we have counts for
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Optimal Conformal Prediction under Epistemic Uncertainty
Alireza Javanmardi, Soroush H. Zargarbashi, Santo M. A. R. Thies +3
Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees. In practi…
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner +1
We propose NetGAN - the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased…
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Aleksandar Bojchevski, Stephan Günnemann
Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approa…