  The goal of network embedding is to transform nodes in a network to a
low-dimensional embedding vectors. Recently, heterogeneous network has shown to
be effective in representing diverse information in data. However,
heterogeneous network embedding suffers from the imbalance issue, i.e. the size
of relation types (or the number of edges in the network regarding the type) is
imbalanced. In this paper, we devise a new heterogeneous network embedding
method, called BHIN2vec, which considers the balance among all relation types
in a network. We view the heterogeneous network embedding as simultaneously
solving multiple tasks in which each task corresponds to each relation type in
a network. After splitting the skip-gram loss into multiple losses
corresponding to different tasks, we propose a novel random-walk strategy to
focus on the tasks with high loss values by considering the relative training
ratio. Unlike previous random walk strategies, our proposed random-walk
strategy generates training samples according to the relative training ratio
among different tasks, which results in a balanced training for the node
embedding. Our extensive experiments on node classification and recommendation
demonstrate the superiority of BHIN2vec compared to the state-of-the-art
methods. Also, based on the relative training ratio, we analyze how much each
relation type is represented in the embedding space.
