  We address the problem of uncertainty quantification for graph-structured
data, or, more specifically, the problem to quantify the predictive uncertainty
in (semi-supervised) node classification. Key questions in this regard concern
the distinction between two different types of uncertainty, aleatoric and
epistemic, and how to support uncertainty quantification by leveraging the
structural information provided by the graph topology. Challenging assumptions
and postulates of state-of-the-art methods, we propose a novel approach that
represents (epistemic) uncertainty in terms of mixtures of Dirichlet
distributions and refers to the established principle of linear opinion pooling
for propagating information between neighbored nodes in the graph. The
effectiveness of this approach is demonstrated in a series of experiments on a
variety of graph-structured datasets.
