Dynamic heterogeneous graph embedding using hierarchical attentions

Abstract

Graph embedding has attracted many research interests. Existing works mainly focus on static homogeneous/heterogeneous networks or dynamic homogeneous networks. However, dynamic heterogeneous networks are more ubiquitous in reality, e.g. social network, e-commerce network, citation network, etc. There is still a lack of research on dynamic heterogeneous graph embedding. In this paper, we propose a novel dynamic heterogeneous graph embedding method using hierarchical attentions (DyHAN) that learns node embeddings leveraging both structural heterogeneity and temporal evolution. We evaluate our method on three real-world datasets. The results show that DyHAN outperforms various state-of-the-art baselines in terms of link prediction task.