We consider the problem of node classification in heterogeneous graphs where both nodes and relations may be of different types and a different set of categories is associated to each node type. When graph node classification has mainly been addressed for homogeneous graphs, heterogeneous classification is a recent problem which has been motivated by applications in fields such as social networks where the graphs are intrinsically heterogeneous. We propose a transductive approach to this problem based on learning graph embeddings and model the uncertainty associated to the node representations using Gaussian embeddings. A comparison with representative baselines is provided on three heterogeneous datasets.