Multilabel classification on heterogeneous graphs with Gaussian embeddings

Abstract

We consider the problem of node classification in heterogeneous graphs, where both nodes and relations may be of different types, and different sets of categories are associated to each node type. While graph node classification has mainly been tackled for homogeneous graphs, heterogeneous classification is a recent problem which has been motivated by applications in fields such as social networks, where 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.