  Graph-based semi-supervised learning is a fundamental machine learning
problem, and has been well studied. Most studies focus on homogeneous networks
(e.g. citation network, friend network). In the present paper, we propose the
Heterogeneous Embedding Label Propagation (HELP) algorithm, a graph-based
semi-supervised deep learning algorithm, for graphs that are characterized by
heterogeneous node types. Empirically, we demonstrate the effectiveness of this
method in domain classification tasks with Facebook user-domain interaction
graph, and compare the performance of the proposed HELP algorithm with the
state of the art algorithms. We show that the HELP algorithm improves the
predictive performance across multiple tasks, together with semantically
meaningful embedding that are discriminative for downstream classification or
regression tasks.
