  A heterogeneous information network (HIN) has as vertices objects of
different types and as edges the relations between objects, which are also of
various types. We study the problem of classifying objects in HINs. Most
existing methods perform poorly when given scarce labeled objects as training
sets, and methods that improve classification accuracy under such scenarios are
often computationally expensive. To address these problems, we propose ConCH, a
graph neural network model. ConCH formulates the classification problem as a
multi-task learning problem that combines semi-supervised learning with
self-supervised learning to learn from both labeled and unlabeled data. ConCH
employs meta-paths, which are sequences of object types that capture semantic
relationships between objects. ConCH co-derives object embeddings and context
embeddings via graph convolution. It also uses the attention mechanism to fuse
such embeddings. We conduct extensive experiments to evaluate the performance
of ConCH against other 15 classification methods. Our results show that ConCH
is an effective and efficient method for HIN classification.
