June 7, 2024, 4:42 a.m. | Dominik Fuchsgruber, Tom Wollschl\"ager, Stephan G\"unnemann

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04043v1 Announce Type: new
Abstract: In domains with interdependent data, such as graphs, quantifying the epistemic uncertainty of a Graph Neural Network (GNN) is challenging as uncertainty can arise at different structural scales. Existing techniques neglect this issue or only distinguish between structure-aware and structure-agnostic uncertainty without combining them into a single measure. We propose GEBM, an energy-based model (EBM) that provides high-quality uncertainty estimates by aggregating energy at different structural levels that naturally arise from graph diffusion. In contrast …

abstract arxiv cs.lg data domains energy gnn graph graph neural network graph neural networks graphs issue network networks neural network neural networks stat.ml them type uncertainty

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