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The goal of network embedding is to transform nodes in a network to alow-dimensional embedding vectors. Recently, heterogeneous network has shown tobe effective in representing diverse information in data. However,heterogeneous network embedding suffers from the imbalance issue, i.e. the sizeof relation types (or the number of edges in the network regarding the type) isimbalanced. In this paper, we devise a new heterogeneous network embeddingmethod, called BHIN2vec, which considers the balance among all relation typesin a network. We view the heterogeneous network embedding as simultaneouslysolving multiple tasks in which each task corresponds to each relation type ina network. After splitting the skip-gram loss into multiple lossescorresponding to different tasks, we propose a novel random-walk strategy tofocus on the tasks with high loss values by considering the relative trainingratio. Unlike previous random walk strategies, our proposed random-walkstrategy generates training samples according to the relative training ratioamong different tasks, which results in a balanced training for the nodeembedding. Our extensive experiments on node classification and recommendationdemonstrate the superiority of BHIN2vec compared to the state-of-the-artmethods. Also, based on the relative training ratio, we analyze how much eachrelation type is represented in the embedding space.

BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network