Dynamic Heterogeneous Information Network Embedding in Hyperbolic Space

Abstract

Heterogeneous information network (HIN) embedding, aiming to project HIN into a low-dimensional space, has attracted considerable research attention.Existing heterogeneous graph representation learning methods also take temporal evolution into consideration in Euclidean space which, however, underestimates the inherent complex and hierarchical properties in many real-world temporal networks, leading to sub-optimal embeddings.To explore these properties of a dynamic heterogeneous network, we propose a dynamic hyperbolic heterogeneous embedding(DyHHE) model that fully takes advantage of the hyperbolic geometry and structural heterogeneity.More specially, to capture the structure and semantic relations between nodes, we employ the meta-path guided random walk to sample the sequences for each node.Then DyHHE maps the temporal graph into hyperbolic space, and capture the structural heterogeneity and evolving behaviors by facilitating the proximity measurement.Experimental results on two real-world datasets demonstrate the superiority of DyHHE, as it consistently outperforms competing methods in link prediction task.